Interested in this AI/ML Engineer role at Tata Consultancy Services (TCS)?
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About This Role
Must Have Technical/Functional Skills:
Digital Marketing; MSBI; Database Administration (DBMS); Cosmos; Digital: Big Data Platforms
Hands\-on co\-ordination and execution of digital marketing programs with experience in email and mobile app channels.
This includes ownership of segmentation for various marketing programs including validation and testing.
Collaborate with the operations team ensuring requirements are accurate and delivered on time.
Collaborate with other teams (agencies \& data) to maximize available resources for client support and successful outcomes.
Track ongoing progress of projects and deliver timely updates to clients.
Manage all technical aspects of marketing automation and CRM integration. Communicate issues and technical roadblocks effectively
Be knowledgeable on digital marketing best practices, technologies and trends. CAN\-SPAM best practices and GDPR compliance is followed.
Monitor internal testing results and industry best practices. Integrate learning to ensure optimal results, improved deliverability, and compliance around permission\-based marketing
Monitor daily campaign deliverability, ensuring high inbox success rate for all campaigns.
Leverage campaign management best practices to optimize campaign performance, including techniques like A/B or multivariate testing, personalization, and segmentation
Work with engineering partners to manage new data requirements, and rationalize the campaign roadmap accordingly
Technical/Functional Skills:
6\+ years of digital marketing campaign management, ability to multitask effectively, and work cross\-functionally
2\+ years of working with Salesforce Marketing Cloud, Adobe Campaign Standard, or Braze.
Experience working with one of the following: SQL, Cosmos DB, Oracle.
Proficient in collaborating with a globally dispersed team spanning diverse geographic regions and time zones.
Experience working with audience criteria and execution of list deployment.
Experience supporting email marketing and mobile (in\-app and push) channels
Ability to thrive in a fast\-paced, fluid environment
Intermediate MS Excel
Ability to multi\-task and manage stakeholders within a dynamic business environment
Extremely detail\-oriented approach with focus on data quality
Extensive understanding of digital marketing business practices
Knowledge of end\-to\-end campaign management processes
Excellent technical \& analytical skills with strong business acumen
Previous experience working with Microsoft will be an added advantage
Passion to drive improvement in the overall customer experience
Experience working across multiple agencies/stakeholders and teams
Familiar with project management tra cking tools like Azure Dev Ops, MS Projects, Jira, etc.
TCS Employee Benefits Summary:
Discretionary Annual Incentive.
Comprehensive Medical Coverage: Medical \& Health, Dental \& Vision, Disability Planning \& Insurance, Pet Insurance Plans.
Family Support: Maternal \& Parental Leaves.
Insurance Options: Auto \& Home Insurance, Identity Theft Protection.
Convenience \& Professional Growth: Commuter Benefits \& Certification \& Training Reimbursement.
Time Off: Vacation, Time Off, Sick Leave \& Holidays.
Legal \& Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, Colleg e Fund, Student Loan Refinancing.
Salary Range: $64,00 0\-$110,000 a year
\#LI\-NS4
Location
San Antonio, TX
Job Function
TECHNOLOGY
Role
Senior Engineer
Job Id
405471
Desired Skills
DBMS \| Marketing \| Cloud Azure
Salary Range
$64,000\-$110,000 a year
Salary Context
This $64K-$110K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Tata Consultancy Services (TCS), 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($87K) sits 48% below the category median. Disclosed range: $64K to $110K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Tata Consultancy Services (TCS) AI Hiring
Tata Consultancy Services (TCS) has 50 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Scientist, AI Product Manager. Positions span Westfield, NJ, US, New York, NY, US, Durham, NC, US. Compensation range: $90K - $380K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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