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About This Role
Application
*Solvd Inc. is a rapidly growing AI\-native consulting and technology services firm delivering enterprise transformation across* *cloud, data, software engineering, and artificial intelligence**. We work with industry\-leading organizations to design, build, and operationalize technology solutions that drive measurable business outcomes.*
*Following the acquisition of* *Tooploox, a premier AI and product development company, Solvd now offers true* *end\-to\-end delivery****—from strategic advisory and solution design to custom AI development and enterprise\-scale implementation. Our capability centers combine deep technical expertise, proven delivery methodologies, and sector\-specific knowledge to address complex business challenges quickly and effectively.*
We are looking for a Forward Deployed Engineer to work directly with clients on high ambiguity GenAI, agentic AI and AI\-enabled workflow transformation initiatives. This role is designed for someone who can operate at the intersection of customer discovery, hands\-on engineering, solution architecture, early delivery and production adoption.
You will work closely with client stakeholders and users to uncover the real operational problem, build thin end\-to\-end solutions with real systems and data, validate feasibility, shape the delivery path and stay involved through MVP or first production release to assure alignment with expected outcome.
Role mission
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Your mission is to turn ambiguous customer problems into validated, production\-grade AI solutions.
You will help Solvd move from “we can build this” to “we have proven this works in the customer’s real operating environment.” You will bridge presales, discovery, prototyping and delivery, while converting field learnings into solution blueprints and service accelerators.
Your responsibilities
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- Lead discovery and solution shaping for GenAI / Agentic AI initiatives, working directly with client stakeholders, users and technical teams to understand workflows, systems, data and business constraints.
- Drive early\-stage opportunities partnering with account executives, product analysts and technology SMEs to prototype, define scope, implementation roadmap and effort estimates.
- Translate ambiguous business problems into validated AI solution designs covering model strategy, data access, orchestration, tool use, integration patterns and production constraints.
- Build proof\-of\-concepts fast, prioritizing speed and impact over perfect design, technical spikes to validate architectural assumptions using real or representative systems and data.
- Evaluate and select appropriate AI frameworks, platforms and tools, including LLMs, vector databases, orchestration frameworks, cloud AI services and agentic tooling.
- Present, defend and explain complex AI, architecture and delivery concepts in simple, practical terms for both business and technical stakeholders.
- Stay involved through MVP or first production release to preserve context, support delivery teams and validate that the solution works in the customer’s operating environment.
- Define evaluation criteria for AI quality and business impact, including accuracy, groundedness, tool\-call correctness, latency, cost, adoption and workflow effectiveness.
- Ensure proposed solutions adhere to security, compliance, Responsible AI, observability and production\-readiness principles.
- Convert field learnings into reusable Solvd assets, including solution blueprints, evaluation frameworks and service accelerators.
Our requirements
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- A seasoned hands\-on technical leader who has evolved from full\-stack or server\-side software engineering into the GenAI and agentic AI domain.
- Experienced in designing, prototyping and integrating systems that combine LLMs, agentic frameworks, enterprise data and business workflows.
- Proficient at combining established engineering patterns with emerging AI capabilities to deliver reliable, production\-ready solutions.
- Skilled in guiding teams and clients through ambiguity \- able to prototype rapidly, validate assumptions and converge toward delivery\-ready designs.
- Comfortable engaging with executives, product teams, operational users and engineers alike, translating complex AI concepts into clear, actionable solution narratives.
- Pragmatic, curious, collaborative and obsessed with customer problem — equally focused on technical soundness, business value, user adoption and delivery readiness.
- 8\+ years of experience in IT, including strong experience in software design and engineering.
- 2\+ years of hands\-on experience architecting or building GenAI or agentic AI systems using modern LLM ecosystems, such as OpenAI, Anthropic, Gemini, Azure AI or AWS Bedrock.
- Experience supporting presales solutioning, discovery, prototyping or early delivery for AI engagements.
- Strong understanding of LLM orchestration, retrieval\-augmented generation, vector databases, prompt engineering and tool\-calling patterns.
- Understanding of cost modeling for AI workloads, including token usage, inference scaling and hosting models.
- Proficiency with at least one major cloud platform, such as AWS, Azure or GCP, and its AI/ML service offerings.
- Demonstrated experience leading technical discussions with both engineering and non\-technical stakeholders in presales, discovery or early delivery phases.
- Strong command of software engineering fundamentals, API design, integration patterns and production\-readiness practices.
- Knowledge of modern software delivery practices, including CI/CD, containerization, observability and DevSecOps.
- Excellent communication, presentation and documentation skills, with the ability to articulate complex solutions clearly and persuasively.
Optional
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- Prior experience as a Forward Deployed Engineer, Field Engineer, staff engineer, solution architect or technical product lead.
- Prior experience with traditional AI/ML systems, including model training, MLOps, data pipelines or feature stores.
- Familiarity with frameworks for agentic or multi\-component AI pipelines.
- Exposure to Responsible AI, data privacy and governance frameworks.
- Hands\-on experience with Databricks or Snowflake.
- Experience creating reusable solution blueprints, evaluation frameworks or technical accelerators.
- Industry\-recognized cloud and AI certifications from Anthropic, AWS, Azure or Google.
When you join Solvd, you'll…
- Shape real\-world AI\-driven projects across key industries, working with clients from startup innovation to enterprise transformation.
- Be part of a global team with equal opportunities for collaboration across continents and cultures.
- Thrive in an inclusive environment that prioritizes continuous learning, innovation, and ethical AI standards.
Ready to make an impact?
If you're excited to build things that matter, champion responsible AI, and grow with some of the industry’s sharpest minds. Apply today and let’s innovate together.
*Solvd is an equal opportunity employer.*
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Solvd, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Solvd AI Hiring
Solvd has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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