Interested in this AI/ML Engineer role at Tata Consultancy Services (TCS)?
Apply Now →Skills & Technologies
About This Role
Data \& AI Engineer
- 4\-6 years of Data Engineering experience with proven hands\-on experience in Big Data technologies and Business Intelligence tools.
- Understanding of Agile or other rapid application development methods. Exposure to design and coding across one or more platforms and languages as appropriate.
- Exposure to application design, software development, and automated testing methodologies.
- Experience with CI/CD practices using Git, Jenkins, and code integration/review processes.
- Backend platform: Experience building data connectors with relational \& nonrelational backend databases including MongoDB, PostgreSQL, and custom API Connectors.
- Google Cloud Platform Expertise: Hands\-on experience with GCP services like BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Composer (Airflow DAGs), and Cloud Functions.
- Programming Languages: Strong proficiency in Python and SQL for data ingestion, processing, and manipulation.
- ETL/ELT: Experience with ETL/ELT processes and data warehousing concepts.
- Data Modeling: Understanding of data modeling principles and techniques.
- Cloud Technologies: Knowledge of cloud computing concepts and standard processes.
- AI / ML: Experience with Statistical / Predictive Modeling, Machine Learning, A/B testing, and designing feature experiments
- AI Frameworks: •Experience supporting AI integrations ML pipelines including BigQuery ML, or other GCP AI/ML services
- BI Tools: Experience working with BI tools including Tableau and PowerBI
- Problem\-Solving: Excellent analytical and problem\-solving skills.
- Experience working with large datasets, ODL, identifying ingestion issues and proactively collaborating to resolve data related issues.
- Passion for travel/airports a plus.
- Experience with LLMs, Generative AI, or NLP systems.
- Familiarity with feature stores and model registries.
- Knowledge of data governance and compliance standards.
- Experience with real\-time analytics and streaming architectures.
- Certifications in cloud or AI technologies.
- Design, build, and maintain scalable data pipelines (batch and streaming) supporting real\-time / near\-real\-time data integration for Lounge Services Analytics use cases.
- Develop and optimize ETL/ELT processes for structured and unstructured data from various backend DBs and sources including MongoDB, PostgreSQL, APIs, and third\-party systems.
- Architect and manage cloud\-native data infrastructure/warehouses within Google Cloud Platform (BigQuery) environments.
- Orchestrate Data Transformations with Cloud Composer / Airflow DAGs.
- Enable trusted, analytics\-ready datasets to support Tableau dashboards and analytics.
- Experience with observability and monitoring tools such as Datadog, CloudWatch, Prometheus, or similar platforms.
- Implement data monitoring, logging, and observability frameworks to ensure data quality, integrity, governance and security standards are met.
- Build AI / ML models and integrations to support predictive \& personalized use cases including lounge recommendations, capacity forecasting, and lounge optimization
- Collaborate with cross\-functional teams including Analytics, Product \& Business to define data requirements and deliver solutions.
- Optimize data storage and processing for performance and cost\-efficiency (partitioning and indexing solutions).
- Implement and enforce data governance standards, including data classification, access controls, and retention policies.
- Develop and maintain metadata management practices, including data lineage, catalog documentation, and business glossary alignment.
Salary Range\- $100,000\-$120,000 a year
Location
New York, NY
Job Function
TECHNOLOGY
Role
Engineer
Job Id
403669
Desired Skills
Big Data
Salary Range
$100,000\-$120,000 a year
Desired Candidate Profile
Qualifications : BACHELOR OF COMPUTER SCIENCE
Salary Context
This $100K-$120K range is above 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. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($110K) sits 34% below the category median. Disclosed range: $100K to $120K.
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
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% above 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.