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About This Role
New York, New York
Job Summary
As an Agentic Forward Deployed Engineer, you operate at the front line of delivery \- embedded with the client, turning ambiguous business problems into production agents, fast. Your deliverable is Business Transformation Agents: autonomous and multi\-agent systems that automate and reimagine real business processes such as invoice disputes, procurement approvals, onboarding, claims and compliance workflows. You own each agent end to end \-conceptualize, build, integrate, evaluate, deploy, and sustain \- and you lead a small team to do the same. You build exclusively in Python using agent development kits, and you bring Agentic AI capabilities to life inside the client's world, with Responsible AI, evaluation and security as non\-negotiables
Key Responsibilities
- Conceptualize fast: embed with stakeholders, frame a business process as an agentic solution, and stand up a working agent prototype in days, not weeks.
- Build Business Transformation Agents: design and ship single\-agent and multi\-agent systems in Python using ADKs that automate and transform real client workflows, with measurable ROI.
- Own efficiency as the scorecard: drive delivery efficiency and operational efficiency ; shorter cycle times, less manual effort, higher accuracy, lower cost\-to\-serve.
- Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG / context\-graph retrieval, memory, tool / function calling, MCP integration and multi\-agent orchestration.
- Integrate to standards: connect agents into client ecosystems through proven integration patterns, standards\-based APIs and secure authentication.
- Make reusability and predictability the default: build reusable agent components, skills, tool libraries and templates; add guardrails so agent behaviour is predictable, safe and repeatable.
- Prototype and iterate quickly: use the kit's scaffolding to prototype, then harden to production\-grade, well\-tested Python.
- Run eval\-driven development: build evaluation harnesses and test suites that measure agent correctness, safety and regression before anything ships.
- Own AgentOps / DevSecOps: CI/CD for agents, versioning, observability and telemetry, shift\-left security, and Responsible AI governance baked in from day one.
- Run a continuous, adaptable feedback loop: feed production telemetry, evals and client feedback back into prompts, context and agent design.
- Stay ahead of the curve: adopt evolving agent frameworks and patterns quickly, and bring field learnings back to the practice.
- Lead and mentor: set technical direction for a lean team of 3 agent engineers, raise the engineering bar, and grow the pod's agentic capability.
Skill Requirements
Language: Python
Frameworks: Agent Development Kits (ADKs) ; e.g. Google ADK, LangGraph, CrewAI, OpenAI Agents SDK, AWS Bedrock AgentCore, Microsoft Agent Framework / Semantic Kernel. Framework choice follows the engagement; the discipline is the same.
Models: Multi\-LLM via the kit (e.g. Claude on Bedrock, Gemini, Azure OpenAI), selected per use case for quality, latency and cost.
Interfaces: Tools and Model Context Protocol (MCP) for integration; standards\-based APIs and secure auth for client systems.
Must Have Skills
- Strong Python engineering ; idiomatic, typed, tested and packaged code; on a foundation of solid software engineering principles (design, version control, architecture).
- Hands\-on agent building with at least one agent development kit (Google ADK, LangGraph, CrewAI, OpenAI Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework / Semantic Kernel).
- Solid command of agent engineering: prompt engineering, context engineering, prompt caching, RAG / context graphs, tool / function calling, MCP, and multi\-agent orchestration.
- Eval\-driven development: designing evaluation harnesses and measuring agent quality, safety and reliability.
- Standards\-based integration and DevSecOps: APIs, secure auth, CI/CD, observability and AgentOps.
- Ability to conceptualize a business problem as an agent quickly, and operate effectively in ambiguous, customer\-embedded settings.
- Client\-facing maturity: translates fluidly between technical and non\-technical stakeholders, and owns outcomes.
- Experience mentoring or leading small engineering teams.
Other Requirements
Preferred Skilla
- Fluency across multiple ADKs and the judgment to pick the right one per engagement.
- Deploying agents to managed runtimes at enterprise scale (e.g. Vertex AI Agent Engine, Bedrock AgentCore) with governance and cost control.
- Domain depth in a transformation area \- finance operations, supply chain, HR, claims or compliance.
- Experience with an enterprise agent platform, including Responsible AI and governance at scale.
- A track record of turning agents into reusable accelerators or IP adopted beyond a single engagement.
Maximum Salary (US): 234000
Minimum Salary (US): 132000
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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 HCLTech, 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.
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.
HCLTech AI Hiring
HCLTech has 5 open AI roles right now. They're hiring across AI Product Manager, Data Scientist, AI/ML Engineer. Positions span US, St. Louis, MO, US, San Antonio, TX, US.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 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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