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About This Role
*Reports to:Chief Technology Officer*
- *Requires minimum of 2 days a week onsite in Boston, MA office*
*Base salary range: $135,000 to $170,000 USD, plus annual bonus. Final compensation will be determined based on experience, skills, qualifications, and relevant market data.*
About the Opportunity
IANS is rapidly expanding its AI strategy toward a Data\-as\-a\-Service (DaaS) model, delivering not only insights through our own applications, but also structured data feeds, APIs, and AI ready interfaces that enable clients to build their own intelligent systems and agentic workflows.
We are seeking an Agentic Engineer to build and ship the agent capabilities at the heart of IANS' agentic AI platform. This is a hands\-on builder's role: working within the platform architecture set by our senior and principal engineers, you will implement, test, and operate the agents, tools, retrieval components, and evaluation suites that power both our internal AI products and the client\-facing agent platform that consumes IANS data feeds. You will own well\-scoped features end\-to\-end — from design through production — and grow your scope as you demonstrate ownership and judgment.
This is an exceptional opportunity to do serious agentic engineering early in your career: you will work daily with engineers who have shipped production multi\-agent systems, on a platform where evaluation, observability, and security are first\-class concerns rather than afterthoughts.
What You'll Do:
Agentic Systems Development
- Build, test, and ship production\-quality agent capabilities — tools, retrieval components, memory features, orchestration steps, and evaluation coverage — within the platform's established architecture.
- Implement agent and tool\-use patterns with frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or comparable systems, with regression coverage and observability included in every change.
- Integrate retrieval\-augmented generation (RAG), structured tool use, MCP\-style tool protocols, and APIs into robust, enterprise\-grade platform components.
- Contribute to the developer\-facing primitives that allow external clients to safely extend the IANS agent platform with their own proprietary data and workflows.
Evaluation, Observability \& Quality
- Extend benchmarking, regression testing, and observability suites that measure agent quality, latency, cost, reliability, and safety, using modern AI observability tooling such as LangSmith, Langfuse, Arize, or Weights \& Biases.
- Write and maintain evaluations for agentic behaviors — tool\-use correctness, hallucination rates, multi\-turn coherence, and task completion — for the features you ship.
- Investigate eval regressions and production incidents in agent behavior, and land the fixes.
Collaboration \& Growth
- Participate actively in design reviews and code reviews, absorbing and applying feedback from senior and principal engineers.
- Take progressively larger ownership as you build a track record, from features to subsystems.
- Stay current on the rapidly evolving agentic AI landscape (frontier models, orchestration frameworks, evaluation standards, agent protocols) and share what you learn with the team.
Software Engineering \& Systems Integration
- Ship production code across the stack: Go for services and agent runtimes, Python for AI/ML workflows, and React with TypeScript for customer\-facing and internal web applications.
- Build the APIs and microservices that internal teams and external clients use to integrate with IANS' data and agent platforms.
- Test, log, trace, and performance\-tune everything you ship, including agent workflows and model\-driven systems.
AI Infrastructure \& Deployment
- Deploy and operate services on IANS' AWS\-based agent infrastructure, including AWS ECS agent runtimes, AWS Bedrock for foundation model access, and AWS Lambda for tool execution.
- Contribute to the data pipelines, vector databases, and retrieval systems that support RAG, agent memory, embeddings, and inference at scale.
- Instrument token usage, latency, and inference cost for the features you own.
Security \& Enterprise\-Grade Standards
- Follow IANS' standards for security, data isolation, governance, observability, and cost control in everything you ship, especially where agent capabilities and IANS data products are exposed to clients.
- Implement access controls, sandboxing, and audit logging requirements in the components you build.
- Build in compliance with regulatory frameworks (GDPR, CCPA, etc.) and IANS' SOC 2 Type II controls, and uphold responsible AI practices.
What You Bring:
Required
- 3–5 years of software engineering experience, including hands\-on work building LLM powered features or agentic systems that reached production users.
- Proficiency in Go, or strong proficiency in a comparable systems language (Rust, TypeScript, Java, C\#) with the ability to become productive in Go quickly; working knowledge of Python for AI/ML workflows.
- Experience building web applications with React (or a comparable modern frontend framework) and TypeScript.
- Experience building and shipping LLM\-powered applications — whether with an agent framework such as LangChain, LangGraph, LlamaIndex, or AutoGen, or directly against model APIs. Strong engineering fundamentals matter more to us than any particular.
- Working understanding of RAG, embeddings, vector databases, and prompt/context.
- Familiarity with cloud infrastructure, ideally AWS (ECS, Lambda, Bedrock, or comparable services).
- Strong testing and debugging discipline, and the habit of instrumenting what you ship.
- Clear written and verbal communication and a demonstrated appetite for feedback and growth.
Nice to Have
- Exposure to AI evaluation and observability tooling such as LangSmith, Langfuse, Arize, or Weights \& Biases.
- Experience with MCP\-style tool protocols or building tools for AI agents.
- Experience operating services in production (on\-call, incident response, performance tuning).
- Familiarity with fine\-tuning or post\-training techniques (LoRA, PEFT, RLHF, DPO).
- Contributions to open\-source AI or agent tooling.
- Cybersecurity domain familiarity.
Agentic engineering is a young discipline, and few candidates will check every box above. If this role excites you and you can show us strong engineering fundamentals and real work with LLM powered systems, we encourage you to apply even if you don't meet every single qualification— research shows that people from underrepresented groups often rule themselves out prematurely, and we'd rather make that call together.
Salary Context
This $135K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At IANS, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($152K) sits 30% below the category median. Disclosed range: $135K to $170K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
IANS AI Hiring
IANS has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $170K - $180K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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