Interested in this AI/ML Engineer role at Tata Consultancy Services (TCS)?
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About This Role
Must Have Technical/Functional Skills:
Generative AI (Gen AI), Agentic AI
Model selection, evaluation, interpretability: TensorFlow, PyTorch, Hugging Face, NLP, computer vision, time\-series modeling.
AI Strategy, Architecture, and Roadmap Planning
Python / R / TypeScript Programming
AI Frameworks (LangChain, AutoGen, Azure AI Foundry, Azure AI Agent, CrewAI, LangGraph, Google ADK)
Model Context Protocol (MCP), Agent to Agent Protocol
N\-8\-N
GuardRails, AI Ethics and Regulations
Prompt Engineering: “Expertise in prompt engineering, LLM operations, and GenAI deployment best practices.”
Distillation, RAG, Fine\-tuning
Multi\-modal AI, LLMs
Vector Databases, Embeddings
GenAI deployment tools (Docker, Kubernetes)
AI Solution Assessment and Optimization
ETL, Data Pipelines, Feature Stores: Experience with tools like Airflow, dbt, MLflow, DVC, Azure ML pipelines.
CI/CD for ML: Automated model retraining, versioning, and monitoring.
Data anonymization, privacy\-by\-design, secure model deployment: Especially for regulated industries (GDPR, SOX, HIPAA).
Desirable certifications: AI/ML, cloud architecture, relevant technology (e.g., GCP GenAI Leader, AWS AI Practitioner, Azure AI certifications)
Roles \& Responsibilities:
Regional AI Technical Lead is a strategic technical leader responsible for designing, implementing, and managing advanced generative AI solutions across Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). This role combines deep expertise in generative AI technologies, multi\-cloud architecture, and modern software engineering practices to deliver scalable, secure, and innovative AI solutions. The architect leads cross\-functional teams, drives technical vision, and ensures alignment between business objectives and AI initiatives.
Strategy \& Roadmap
- Define and drive the AI strategy, aligning with business goals and innovation priorities.
- Develop and maintain the AI solution roadmap, including short\-term deliverables and long\-term vision.
- Evaluate emerging AI trends and technologies to inform strategic direction.
Architecture \& Design
- Architect end\-to\-end AI solutions using Gen AI, Agentic AI, LLMs, and multi\-modal AI.
- Design intelligent agent systems using LangChain, LangGraph, Model Context Protocol (MCP), Agent to Agent Protocols, Google AI Development Kit (ADK), Azure AI Studio or AWS Bedrock.
- Integrate large language models (LLMs) such as Llama, Gemini, GPT, and Claude into enterprise systems and custom applications.
- Establish scalable and modular AI architectures that support RAG pipelines, Vector DBs (ChromaDB, Pinecone, FAISS, Weaviate, Vertex AI Matching Engine).
- Develop and optimize retrieval\-augmented generation (RAG) pipelines using vector databases
- Define and enforce AI governance frameworks, including Responsible AI, GuardRails, and compliance w ith AI Ethics \& Regulations.
- Supervise the design, fine\-tuning, and optimization of generative AI models and multimodal systems (text, image, audio).
- Lead Python\-based development for prompt orchestration, tool agents, APIs, and data pipelines.
Assessment \& Optimization
- Conduct technical assessments of existing AI/ML systems, models, and data pipelines.
- Identify gaps, risks, and opportunities for modernization or enhancement.
- Recommend architectural improvements and integration strategies for legacy systems.
Cloud Platform Integration \& Management
- Architect, deploy, and monitor generative AI solutions on GCP (Vertex AI, Document AI, AlloyDB, BigQuery, Cloud Run), Azure (OpenAI Service, Cognitive Search, Azure ML, Azure Functions), and AWS (Bedrock, SageMaker, Lambda, API Gateway, DynamoDB).
- Design and manage scalable cloud infrastructure, ensuring performance, cost efficiency, and compliance across platforms.
- Implement containerization and orchestration strategies using Docker and Kubernetes (GKE/EKS/AKS) for reliable deployment.
- Establish and enforce security frameworks using GCP IAM, Azure Identity, AWS IAM, and related tools for secure, compliant access.
- Utilize monitoring and logging solutions such as Google Cloud Operations Suite, Azure Monitor, and AWS CloudWatch.
MLOps, DevOps \& Governance
- Automate model deployment, versioning, and monitoring using MLOps/DevOps best practices and CI/CD pipelines.
- Implement prompt optimization, context management, and model performance tuning.
- Ensure adherence to data governance, privacy, PII handling, and AI ethics principles throughout the development lifecycle.
Leadership \& Collaboration
- Collaborate with product owners, data scientists, engineers, and business stakeholders.
- Mentor engineering teams and contribute to talent development in AI and ML domains.
- Represent AI architecture in enterprise governance forums and technical councils.
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, College Fund, Student Loan Refinancing.
Salary Range: $80,000 \- $160,000 a y ear
\#LI\-CM2
Location
Seattle, WA
Job Function
TECHNOLOGY
Role
Solution Architect
Job Id
420595
Desired Skills
Artificial Intelligence \| NLP
Salary Range
$80,000\-$160,000 a year
Salary Context
This $80K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($120K) sits 45% below the category median. Disclosed range: $80K to $160K.
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.
Tata Consultancy Services (TCS) AI Hiring
Tata Consultancy Services (TCS) has 16 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Agent Developer. Positions span US, Owings Mills, MD, US, New York, NY, US. Compensation range: $80K - $286K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 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 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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