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About This Role
About BLEN
BLENers are passionate about using technology to solve real-world problems. For over 20 years, we've helped government agencies and businesses transform their digital experience — modernizing legacy systems, building cloud-native applications, and experimenting with what's just around the corner. We value long, enduring partnerships and put humans at the center of every experience. Our team thrives on turning tricky problems into solutions that are practical, accessible, and performant.
About this position
We're hiring an AI Engineer to help our federal and commercial clients ship production-grade applications powered by large language models — with a strong focus on agentic systems and MCP-based integrations.
You'll spend your time building real things: agents that take actions on behalf of users, RAG pipelines that ground answers in trusted sources, and MCP servers that securely connect models to the data and tools our clients already rely on. You'll wire up model APIs, design tool interfaces, build evals, and make sure what we ship is fast, reliable, observable, and safe.
This isn't a research role. You won't be training foundation models. You will be designing and shipping agentic AI systems that real users — including senior government stakeholders — depend on, and you'll have a strong voice in how we adopt generative AI responsibly across our portfolio.
If you get excited about agent design, tool use, MCP, evals, and the weekly firehose of new models and frameworks — and you want that energy pointed at meaningful public-sector work — this is for you.
What You'll Do
- Design and build agentic systems — multi-step agents that plan, call tools, retrieve context, and take action with appropriate human-in-the-loop checkpoints
- Build MCP servers and clients to securely expose client data, internal tools, and APIs to LLMs in a standardized, auditable way
- Ship LLM-powered applications: copilots, document intelligence, search, summarization, and workflow automation
- Design and maintain RAG pipelines — chunking, embeddings, vector stores, retrieval, reranking, and grounding
- Integrate model APIs (OpenAI, Anthropic, Bedrock, Azure OpenAI, open-weight models) and pick the right model for the job based on quality, latency, and cost
- Develop evals and observability for agents and AI features so we know what's working in production and what's regressing
- Apply prompt engineering, structured outputs, function/tool calling, and guardrails to make agent behavior predictable
- Write production Python backends and APIs that expose AI capabilities to web and mobile clients
- Collaborate with engineers, designers, and product folks to scope what AI should (and shouldn't) do in a given product
- Help shape responsible AI practices for federal use — privacy, security, auditability, and human oversight
Basic qualifications
- 5+ years of professional software engineering experience, with at least 1 year shipping LLM-based or AI-powered features to production
- Hands-on experience designing or building agentic systems — tool calling, multi-step reasoning, planning loops, or agent orchestration (LangGraph, CrewAI, OpenAI Agents SDK, Claude tool use, or equivalent)
- Working knowledge of the Model Context Protocol (MCP) — or demonstrated ability to pick it up quickly, plus familiarity with the broader landscape of agent/tool standards
- Strong Python and experience building and deploying backend services and APIs (FastAPI, Flask, or similar)
- Hands-on experience with at least one major LLM provider (OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, or open-weight models via vLLM/Ollama)
- Working knowledge of RAG: embeddings, vector databases (pgvector, Pinecone, Weaviate, Qdrant, or similar), and retrieval evaluation
- Comfort with prompt engineering, structured outputs (JSON mode, schemas), and tool/function calling
- Experience writing evals — even lightweight ones — for non-deterministic systems
- Solid SQL and experience with relational and unstructured data
- Familiarity with at least one cloud platform (AWS, Azure, or GCP)
- Git, code review, and modern collaborative workflows
- Strong written and verbal communication — you can explain AI tradeoffs to non-technical stakeholders
Nice to Have
- Experience authoring MCP servers for non-trivial systems (databases, internal APIs, document stores)
- Experience with eval and observability platforms (Braintrust, LangSmith, Langfuse, Arize, or custom harnesses)
- Multi-agent orchestration patterns and experience reasoning about agent failure modes
- Fine-tuning, distillation, or LoRA experience where it actually moved the needle
- Docker, Kubernetes, and CI/CD for AI workloads
- TypeScript/Node for full-stack AI features
- Streaming UIs (SSE, WebSockets) and token-level UX patterns
- Experience with caching, prompt compression, and cost/latency optimization at scale
- Background supporting federal or government clients
- Awareness of NIST AI RMF, FedRAMP, or related responsible-AI frameworks
Requirements
- Must be a US Citizen or legal resident, able to work domestically
- Must be able to attain a low-level security clearance
- Must work from the United States
Perks
- Work from anywhere.
- The chance to work on high-visibility projects and make a significant impact.
- Competitive salary range ($130,000 - $200,000+) based on role requirements and your experience and impact.
- Comprehensive medical, dental, vision, life, and disability insurance.
- Up to 20+ days of PTO annually.
- 401(k) retirement savings plan.
- FSA/HSA and commuter benefit options.
- Professional development opportunities, certifications, and continuous learning support.
- Company-provided technology and access to productivity and AI tools.
- Additional bonus based on performance.
Get to know us
We're a small, creative, highly technical team. Our heroes are the scrappy folks who dare to dream and do great things. We care more about doing the right thing than taking shortcuts. We finish projects. We surprise our clients with how much we genuinely care about their success. We're selective about our partners — and our people. We don't say "human resource" because you're not a resource. You're a teammate, and we'll treat you like one.
What you should expect from us
- We will treat you fairly
- We give you space to grow personally and professionally
- We will hear your ideas even when we disagree
- We will be honest about our challenges and equitable with our success
- We will tell you the truth, even when it's difficult
What to Expect in Our Hiring Process
Our hiring process typically consists of 3 interview stages, after an initial screening. For engineering roles, the process may include additional technical assessments depending on the position's requirements. We aim to make the process straightforward and respectful of your time. $130,000 - $150,000 a year
This range represents the compensation we expect to offer candidates who can perform the core responsibilities with minimal additional training. Actual compensation may vary based on skill set, experience, certifications, and scope of responsibility.
Equal Opportunity
BLEN is an Equal Opportunity Employer / Protected Veterans / Individuals with Disabilities.
We may use AI tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are made by humans.
We participate in E-Verify. Upon hire, we will provide the federal government with your Form I-9 information to confirm authorization to work in the U.S. Due to the nature of our federal work, all BLEN roles must be performed from the contiguous United States.
We participate in E-Verify. Upon hire, we will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. Due to the nature of our work with the federal government, all roles at BLEN are required to work from the contiguous United States. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $130K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At BLEN, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($165K) sits 22% below the category median. Disclosed range: $130K to $200K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
BLEN AI Hiring
BLEN has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $200K - $200K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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