Interested in this AI/ML Engineer role at Tata Consultancy Services (TCS)?
Apply Now →Skills & Technologies
About This Role
Role: Solution Architect \- Artificial Intelligence
The Agentic AI Architect is a role within TCS’s AI \& Data business unit in the Americas, focused on designing next\-generation AI solutions that leverage autonomous “agentic” AI systems. These systems autonomously make decisions, take actions, adapt to changing environments, and continuously learn. TCS anticipates a shift from traditional chatbots to multi\-agent AI frameworks where multiple agents collaborate to determine actions. This client\-facing consulting position involves shaping AI architecture across various industries, delivering vertical\-specific solutions for domains like BFSI, Manufacturing, Life Sciences, Telecom, Retail, Travel, and Consumer Goods. The role involves thought leadership in emerging Business Units, ensuring TCS’s AI solutions are innovative, scalable, and responsibly engineered.
What You Would Be Doing
- Lead AI Architecture Design: Define end\-to\-end architecture for AI systems incorporating autonomous agents and LLM\-based components, ensuring alignment with business goals.
- Client Workshops \& Strategy: Conduct workshops to understand business requirements and identify opportunities for agentic AI, translating business problems into AI architecture blueprints.
- Multi\-Agent Framework Orchestration: Design frameworks for multi\-agent systems, defining roles and ensuring robust communication and fail\-safes.
- Integration \& Scalability: Outline integration with existing enterprise ecosystems, ensuring scalability and resilience.
- Leverage Prompt Engineering \& RAG: Incorporate advanced prompt engineering techniques and retrieval\-augmented generation (RAG) into solution design.
- Technical Leadership in Delivery: Guide engineering teams through prototyping and solution delivery, troubleshooting high\-level architectural issues.
- Industry\-Tailored Solutions: Customize architectural decisions to industry\-specific requirements, balancing reusability with necessary adaptations.
- Emerging Tech Evaluation: Continuously evaluate new tools and methodologies, integrating them into architecture standards.
- Client Engagement \& Travel: Work closely with client technology leaders, presenting architectural proposals and reviewing technical designs, with travel as required.
- Ethical \& Safe Design: Ensure ethical AI and safety considerations are embedded from the architecture stage, documenting and mitigating potential risks.
What Skills Are Expected
- AI/ML Solution Architecture: Extensive experience in designing and architecting AI or machine learning solutions in an enterprise context.
- Deep Technical Knowledge: Strong understanding of machine learning and AI techniques, especially Generative AI and large language models.
- Multi\-Agent System Design: Knowledge of multi\-agent system patterns and frameworks.
- Prompt Engineering \& RAG: Ability to craft effective prompts and chaining strategies for LLMs, familiar with retrieval\-augmented generation methods.
- AI Ethics \& Responsible AI: Strong grasp of AI ethics and safety principles, able to identify ethical risks and design mitigations.
- Cloud \& Distributed Systems: Deep understanding of cloud architecture and distributed system design.
- Data Management: Solid understanding of data architecture as it relates to AI, including data pipelines, databases, and data lakes.
- Leadership \& Communication: Excellent communication and stakeholder man agement skills, capable of leading discussions with C\-level executives and technical brainstorming with engineers.
- Consulting and Domain Acumen: Prior consulting or client\-facing experience, adept at requirement gathering and crafting proposals.
- Problem\-Solving \& Innovation: Creative mindset to devise innovative solutions leveraging AI agents, strong problem\-solving skills.
- Continuous Learning: Demonstrated habit of continuous learning, staying updated via research papers, conferences, or hands\-on experimentation.
Key Technology Capabilities
- AI \& ML Frameworks: Familiarity with major AI/ML frameworks and services, including OpenAI GPT models, Google PaLM/Vertex AI, and Hugging Face Transformers library.
- SaaS AI \& Data Platforms: Experience with leading SaaS AI \& Data platforms in terms of agentic AI development, implementation, orchestration, AI guardrails
- Agentic AI Tooling: Exposure to frameworks and libraries for building AI agents and chains, such as LangChain ,Microsoft’s Semantic Kernel.
- Retrieval Systems: Strong knowledge of search and retrieval technologies, including vector databases and semantic search.
- Cloud Services: Expertise in cloud ecosystems (AWS, Azure, GCP), including cloud AI services, serverless computing, containerization, and related DevOps tools.
- Programming \& Scripting: Proficiency in programming languages commonly used for AI and integration, primarily Python and at least one general\-purpose language.
- Data Platforms: Knowledge of modern data platforms, including relational databases, NoSQL stores, and data processing frameworks.
- Integration \& APIs: Experience designing and using APIs and middleware, knowledge of event\-driven architectures and message brokers.
- DevOps \& MLOps: Familiar with CI/CD pipelines and infrastructure as code, understanding of MLOps principles and tools.
- Security \& Compliance Tools: Comfort with technologies for securing AI applications, including identity and access management, encryption, and compliance tools.
- Collaboration \& Design: Proficient with tools used in architecture and design documentation, including UML design tools and agile project management tools.
- Emerging Tech: Awareness of emerging tech such as knowledge graphs and reinforcement learning frameworks.
Salary Range: $155,500 \- $247,250 a year
\#LI\-AD1
Location
Gibsonia, PA
Job Function
TECHNOLOGY
Role
Solution Architect
Job Id
404456
Desired Skills
Artificial Intelligence
Salary Range
$155,500\-$247,250 a year
Desired Candidate Profile
Qualifications : BACHELOR OF COMPUTER SCIENCE
Salary Context
This $155K-$247K range is above the 75th percentile 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 ($201K) sits 21% above the category median. Disclosed range: $155K to $247K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.