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About This Role
Job Summary
Forward Deployed Engineer About the role You earn trust by solving the customer’s problem with working agentic AI systems. The work starts close to the user and ends close to production. In this role you design, build, integrate and productionise agentic AI solutions inside customer environments. You turn unclear needs into working systems: AI agents, multi agent workflows, tool and API integrations, retrieval and data pipelines, evaluations and the cloud-native platforms that run them. You own this end to end, from discovery and design through build, integration and production rollout, working alongside the customer’s engineering and domain teams. You work across the major agentic AI stacks rather than a single vendor. Depending on the engagement, that means building with Anthropic, OpenAI, Google or Microsoft, and with vendor-neutral frameworks such as LangGraph where the customer wants to stay portable across models. You prove the idea quickly in a visual builder when that helps the customer. You then move to governed, code-first delivery, where evidence, tests, evaluation and release confidence turn the prototype into verified value. You pick the right model, framework and harness for the customer’s constraints, then make it reliable in their environment. Success is measured by what the customer can run in production and the value it delivers, not by the demo. In the FDE Practice this role covers both value engineering and quality engineering. The value engineering emphasis is implementation and user value. The quality engineering emphasis is evaluation, reliability, release safeguards and production readiness. You may lean one way or the other depending on the engagement. You will use coding agents where they increase speed, but you stay responsible for direction, review and control. You supervise agent output, inspect assumptions, write or refine code and hold the line on enterprise delivery standards.
Key Responsibilities
Location and level • Location: Hybrid, with client-site travel where co-build, discovery or rollout requires it. • Level: Senior. This is a hands-on role for an experienced engineer who can own delivery in front of a customer.
What you will do • Build agentic AI solutions: single and multi-agent systems, tool use, retrieval pipelines, automation and prototypes that reach production. • Build on the major agentic stacks, including the Claude Agent SDK, OpenAI Agents SDK, Google Agent Development Kit, Microsoft Agent Framework and vendor neutral frameworks such as LangGraph, choosing the right one for the customer’s needs. • Work with customer technical teams to understand systems, constraints, data, deployment paths and operating needs. • Use coding agents and human engineering judgement to move quickly while preserving review, test and release discipline. • Integrate agents with enterprise systems, APIs, cloud services, databases, ticketing systems, model services and security controls, often through MCP and similar tooling. • Implement agent evaluations, automated tests, observability hooks, runbooks and release safeguards. • Troubleshoot implementation, deployment, data, model and integration issues. • Codify what works into reusable patterns, starter kits and playbooks, alongside technical decisions, defects, risks and lessons learned, to raise the floor for the wider practice. • Feed field insight back to the platform vendors through our partnerships, turning recurring friction and gaps into product feedback that shapes the tools you build on. • Support handover and production readiness with clear documentation and operational ownership. • Work with architects and delivery managers to keep implementation aligned to value and technical guardrails.
Skill Requirements
What we are looking for These are the essentials. If you meet most of them, we want to hear from you. • Several years of production software engineering experience in Python, TypeScript, Java or comparable stacks. • Hands-on experience building with LLMs and agents on at least one major platform, such as Anthropic, OpenAI, Google or Microsoft. • Practical understanding of agentic patterns: tool use, retrieval, prompting, model behaviour, evaluation and multi-agent workflows such as orchestration, delegation and self-reflection. • Experience with APIs, databases, cloud platforms, integration surfaces or comparable deployment environments. • Strong debugging, testing and integration discipline. • Experience working in ambiguous delivery contexts with customers, users or cross functional technical teams. • Ability to balance fast prototyping with production-quality engineering.
Nice to have These would strengthen your application, but they are not deal-breakers. We do not expect every candidate to bring all of them. • Experience taking agentic or AI systems from prototype to production rollout. • Experience with agent orchestration frameworks, MCP servers or custom tool integrations. • Experience with data and retrieval pipelines, workflow automation or model deployment. • Experience with evaluation, observability, troubleshooting or release safeguards for AI systems. • A vendor certification on one of the major AI platforms (Anthropic, OpenAI, Google or Microsoft).
Must Have Skill
Python Good to Have Skill
Other Requirements
Who does well here The strongest people in this role are: • Hands-on and pragmatic, with a bias towards working software. • Careful about quality, testing, integration and operational consequences. • Comfortable pairing with customers, architects, engineers and agents. • Quick to learn unfamiliar domains, APIs and systems. • Clear in writing, especially around decisions, assumptions, tests and handover notes. • Willing to challenge generated code and weak assumptions rather than accept output at face value. • Focused on user value without ignoring maintainability and production risk.
Salary Context
This $180K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($200K) sits 6% below the category median. Disclosed range: $180K to $220K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
HCLTech AI Hiring
HCLTech has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Santa Clara, CA, US, Bengaluru, Karnataka, India. Compensation range: $148K - $260K.
Location Context
AI roles in Austin pay a median of $209,100 across 306 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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