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About This Role
Aline is the bridge between senior care and technology, built to strengthen connection where it matters most. Our all\-in\-one platform brings together sales, marketing, operations, and engagement tools, empowering senior living communities to work smarter, communicate clearly, and deliver care with heart.
Rooted in industry expertise and born from the merger of leading solutions, Aline serves as a unifying force across the senior care space. We help communities across the country streamline processes, enhance resident and family engagement, and stay aligned through every stage of care. That’s why everything we build is designed to support stronger collaboration, seamless workflows, and more meaningful experiences for residents, families, and care teams alike.
We are looking for a motivated and technically sharp Junior Agentic (AI) Engineer to join Aline’s engineering team. This entry\-level role is designed for engineers with 1–5 years of experience — or strong recent graduates — who have a genuine interest in building production\-grade agentic systems for enterprise workflows. You will work directly with customers and cross\-functional teams to design, build, and ship AI\-powered features that improve outcomes for senior living communities. From architecting multi\-agent workflows to owning the retrieval and eval stack, you will gain hands\-on experience across the full AI product lifecycle — with a focus on reliability, compliance, and measurable impact.
Responsibilities
Agentic Systems \& Orchestration
- Build and deploy agentic systems for enterprise workflows — design and implement AI agents (and multi\-agent systems) that reason and retrieve data across complex business processes and take action in enterprise systems.
- Design and ship multi\-step agentic systems — planner/executor, tool\-using, multi\-agent, and human\-in\-the\-loop — for use cases including onboarding, underwriting, case review, and continuous monitoring.
- Design orchestration, reasoning, and workflows — architect how agents plan, use tools, and coordinate across complex, multi\-step processes.
- Architect agent graphs in LangGraph (or comparable frameworks — CrewAI, AutoGen, Claude Agent SDK) with explicit state, durable execution, retries, and safe fallbacks.
- Expose agents to production systems via well\-typed tools and MCP servers; treat the tool surface area as a product.
Full\-Stack Implementation \& Integrations
- Own full\-stack implementation and integrations — build across LLMs, APIs, backend systems, and lightweight UIs to deliver complete, working solutions.
- Build and own the retrieval layer powering our agents: chunking strategies, hybrid search (vector \+ keyword), reranking, and grounded citation.
- Design and optimize embedding pipelines and vector indexes using pgvector and OpenSearch.
Evaluation, Safety \& Reliability
- Develop agentic harnesses to accelerate development — create evaluation frameworks, toolchains, and workflows that enable rapid iteration and improve system reliability.
- Own the eval stack: curate golden sets, maintain offline regression suites, implement LLM\-as\-judge, and run online A/B and shadow evals.
- Ensure reliability, safety, and production readiness — implement guardrails, validation logic, and fallback mechanisms to ensure consistent and trustworthy behavior in production.
Technology Stack
Languages
Python, Node.js, TypeScript Agent / LLM Frameworks LangGraph, LangChain, Claude Agent SDK, MCP, OpenAI SDK
Models
Anthropic Claude, OpenAI, open\-weight where appropriate Retrieval \& Data PostgreSQL, pgvector, OpenSearch, Kafka, Redshift, Redis
Infrastructure
AWS, Kubernetes (EKS), ArgoCD, Terraform Evals \& Observability LangSmith / Langfuse / Braintrust, DataDog
Qualifications
Education \& Experience
- Bachelor's degree in Computer Science, Data Science, AI/ML, or related field, or equivalent practical experience through projects, research, internships, or professional work.
- 1–5\+ years in software engineering (full\-stack or backend), or a strong recent graduate with demonstrable project or internship experience at equivalent depth.
- Familiarity with LLMs or AI\-based systems.
- Internship or research experience in a production AI or data\-intensive environment is a strong plus.
Required Technical Skills
- Proficiency in Python; comfortable with NumPy, Pandas, and Scikit\-learn.
- Hands\-on experience with at least one LLM framework: LangGraph, LangChain, Claude Agent SDK, or OpenAI SDK.
- Understanding of RAG architecture: embedding models, vector databases, hybrid search, and reranking.
- Familiarity with prompt engineering best practices and awareness of LLM failure modes (hallucination, injection, drift).
- Working knowledge of SQL and relational databases (PostgreSQL, MySQL, or similar).
- Familiarity with Git version control and Agile/Scrum practices.
Preferred
- Experience with agent systems, orchestration frameworks (LangGraph, CrewAI,AutoGen), or AI tooling.
- Exposure to MCP (Model Context Protocol) or building typed tool interfaces for LLM agents.
- Experience with eval frameworks (LangSmith, Langfuse, Braintrust) or building LLM\-as\-judge pipelines.
- Familiarity with cloud platforms — AWS preferred (Azure, GCP also considered).
- Exposure to Kubernetes, Docker, ArgoCD, or Terraform for AI service deployment.
- Exposure to data compliance requirements: SOC 2, GDPR, CCPA, HIPAA.
Soft Skills
- Strong analytical mindset with intellectual curiosity about agents, evals, and production AI behavior.
- This role includes regular interaction with customers to understand workflows, validate solutions, and gather feedback.
- Clear written and verbal communication; able to explain LLM behavior and tradeoffs to diverse audiences.
- Self\-motivated, detail\-oriented, and comfortable operating in a fast\-moving environment.
- Genuine interest in the mission of improving outcomes in senior care through responsible AI.
Candidates should demonstrate experience building LLM\-powered applications, evaluating agent behavior, and working through the full development lifecycle from prototype to production.
This job description is intended as a summary of the primary responsibilities and qualifications for this position. It is not intended as an all\-inclusive list of duties or qualifications that may be required now or in the future.
Visa Sponsorship: Aline is not able to provide visa sponsorship at this time. Applicants must be authorized to work in the United States without sponsorship.
Salary Context
This $65K-$80K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Aline, 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 $178,940 based on 11,900 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,380. This role's midpoint ($72K) sits 59% below the category median. Disclosed range: $65K to $80K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Aline AI Hiring
Aline has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $80K - $80K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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