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About This Role
Why Zensar?
We’re a bunch of hardworking, fun\-loving, people\-oriented technology enthusiasts. We love what we do, and we’re passionate about helping our clients thrive in an increasingly complex digital world. Zensar is an organization focused on building relationships with our clients and with each other—and happiness is at the core of everything we do. In fact, we’re so into happiness that we’ve created a Global Happiness Council, and we send out a Happiness Survey to our employees each year. We’ve learned that employee happiness requires more than a competitive paycheck, and our employee value proposition—grow, own, achieve, learn (GOAL)—lays out the core opportunities we seek to foster for every employee. Teamwork and collaboration are critical to Zensar’s mission and success, and our teams work on a diverse and challenging mix of technologies across a broad industry spectrum. These industries include banking and financial services, high\-tech and manufacturing, healthcare, insurance, retail, and consumer services. Our employees enjoy flexible work arrangements and a competitive benefits package, including medical, dental, vision, 401(k), among other benefits. If you are looking for a place to have an immediate impact, to grow and contribute, where we work hard, play hard, and support each other, consider joining team Zensar!
Zensar is looking for a AI Engineering Leader. This position is open for Full Time with excellent benefits and professional growth opportunities.
What You Will Do
Delivery \& Programme Leadership
Own end\-to\-end delivery of a $10M\+ engineering portfolio across clients — on time, on budget, and to quality bar.
Lead platform build, modernization, and custom application programmes natively on cloud, spanning .NET Full\-Stack, Java Distributed Systems, Python stack etc.
Set and enforce engineering standards: architecture guardrails, code quality, DevSecOps, and release cadence across multi\-team engagements.
Manage programme risk proactively — escalate early, resolve decisively, and keep clients informed throughout.
AI\-Driven Engineering Acceleration
Embed AI tooling across the SDLC — from AI\-assisted requirements and design through to automated testing, code generation, and incident response.
Architect and operationalize agentic systems and workflows that reduce manual toil, accelerate delivery cycles, and improve output quality.
Quantify the impact of AI adoption: establish baselines, track velocity and quality metrics, and present measurable efficiency gains to clients and leadership.
Stay ahead of the AI tooling curve; evaluate and pilot emerging platforms (LLM orchestration, RAG pipelines, AI code assistants).
Portfolio \& Revenue Growth
Carry full P\&L accountability for the portfolio — margin, revenue, forecasting, and commercial hygiene.
Partner with practice, consulting, and client partner leaders to identify expansion opportunities within existing accounts and shape new pursuit strategies.
Translate delivery track record into growth narrative — contribute to proposals, solution designs, and client presentations that differentiate on execution credibility.
Client \& Stakeholder Engagement
Serve as the senior delivery point\-of\-contact for clients — build trust\-based relationships at CTO/CIO/VP level.
Facilitate governance forums (steering committees, QBRs, escalation calls) with clarity and confidence.
Align internal stakeholders — practice heads, resource managers, people leaders — to programme needs without bureaucratic drag.
People \& Capability Development
Lead, mentor, and grow a high\-performing engineering organisation; foster a culture of ownership and continuous improvement.
Champion individual upskilling — create structured learning pathways around AI, cloud, and modern engineering practices.
Spot and develop next\-generation delivery leaders from within the team.
Experience \& Background
15–17 years in software engineering with a significant portion in leadership roles managing multi\-team, multi\-million\-dollar programmes.
Hands\-on track record of delivering platform build, legacy modernization, and greenfield application programmes on cloud — not just oversight, but technical depth you can draw on in client conversations.
Technical Stack \& Architecture
.NET Full\-Stack (C\#, ASP.NET Core, Azure\-native services) and/or Java Distributed Systems (Spring Boot, microservices, Kafka, Kubernetes) — you can assess architecture quality, not just read status reports.
Python stack experience (FastAPI, Django/Flask, pandas, NumPy) particularly for data pipelines, AI/ML integrations, and automation scripts.
Cloud\-native delivery on Azure, AWS, or GCP; Infrastructure as Code, CI/CD pipelines, container orchestration, and observability are second nature.
Practical experience designing and deploying agentic AI systems — LLM orchestration, tool\-use patterns, retrieval\-augmented generation, and multi\-agent workflows in an enterprise context.
AI \& Automation Fluency
Hands\-on experience with enterprise AI coding and productivity tools — GitHub Copilot / Claude (Anthropic), and / or Cursor — applied meaningfully across design, development, review, and documentation phases of the SDLC.
Understands where AI drives automation, acceleration, and efficiency within IT application landscapes — and equally where it introduces risk that must be managed, especially in regulated domains.
Ability to differentiate between AI hype and production\-ready tooling; pragmatic evaluator of what to adopt, when, and how.
Leadership \& Commercial Acumen
Proven P\&L ownership at $10M\+ scale — comfortable with revenue forecasting, margin management, SOW negotiations, and change order governance.
Excellent stakeholder management with both internal leaders and senior client executives; able to hold a room, manage difficult conversations, and build long\-term advisory relationships.
Growth mindset — actively invests in own learning and models the same for the team.
Zensar believes that diversity of backgrounds, thought, experience, and expertise fosters the robust exchange of ideas that enables the highest quality collaboration and work product. Zensar is an equal opportunity employer. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Zensar is committed to providing veteran employment opportunities to our service men and women. Zensar is committed to providing equal employment opportunities for people with disabilities or religious observances, including reasonable accommodation when needed. Accommodation made to facilitate the recruiting process are not a guarantee of future or continued accommodation once hired.
All applicants must be legally authorized to work with Zensar. Visa sponsorship may be available for qualified applicants for certain positions.
Zensar values your privacy. We’ll use your data in accordance with our privacy statement located at: https://zensar.com/privacy\-notice
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 Zensar Technologies, 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.
Zensar Technologies AI Hiring
Zensar Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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