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About This Role
Must Have Technical/Functional Skills
- Be an expert source on machine learning to drive delivery of new and innovative solutions.
- Propose creative solutions to approach business solutions with emerging technologies.
- Prototype new ways of applying technologies for solving business problems.
- Educate others so that they can demonstrate the innovative methods for achieving outcomes.
- Build and maintain machine learning principles, best practices, and code accelerators.
- Conduct external research and internal experimentation for machine learning techniques.
- Champion solution delivery behaviors and approaches from software engineers that accelerate delivery of reliable solutions and create a culture of teamwork.Analyze and communicate strategy, status, and product roadmaps to multiple audiences, including all levels of management.
Roles \& Responsibilities
- GenAI Application Development Expertise
- Programming Languages: Python
- Development Tools: LangChain, LlamaIndex, LangFlow, Langgraph, LangSmith, Flowise
- Techniques: RAG Techniques
- Databases: Vector Databases (Pinecone, Weaviate, Qdrant)
- Additional Technologies: Knowledge Graphs, FastAPI, Streamlit, Gradio
- 2\. Domain Model Fine\-Tuning Capabilities
- Languages \& Libraries: Python, Data Engineering, OSS LLMs (Llama2, Mixtral, GPT\-Neo, GPT\-J)
- Tokenization \& Frameworks: Tokenizers (SentencePiece, Hugging Face Tokenizers), Fine\-tuning Frameworks (Hugging Face Transformers, PyTorch Lightning)
- Datasets: HuggingFace Datasets, TensorFlow Datasets
- 3\. LLMOps Proficiency
- Infrastructure \& CI/CD: DevOps, Kubernetes, Docker, Git, Jenkins, GitLab, GitHub Actions
- Monitoring \& Management: Ray, SeldonCore, MLFlow, MLServer, Triton, BentoML, Prometheus, Grafana
- 4\. Data Engineering for AI Applications
- Data Processing \& Management: Python, Apache Spark, Apache Kafka, AWS S3, Azure Data Lake Storage (ADLS), Delta Lake
- Workflow Automation: Apache Airflow, dbt, Apache NiFi, Fivetran, Airbyte, Great Expectations
- Data Catalogs: Amundsen, Collibra, Alation
- 5\. AI\-Ready Cybersecurity Knowledge
- Threat Modeling \& Security: AI\-Specific Threat Modeling Tools, Secure ML Pipeline Tools, API Security Tools
- Monitoring \& Prevention: AI Security Monitoring Tools, Prompt Injection Prevention Libraries, Adversarial Example Detection Libraries
\- 6\. GenAI Guardrails and Ethics
- Ethics \& Fairness: AI Ethics Frameworks and Tools, Bias Detection and Mitigation Tools, Fairness Metrics Libraries
- Privacy \& Security: Privacy\-Preserving Machine Learning Libraries, Robustness and Security Tools
- Transparency \& Governance: Model Interpretability Libraries, AI Governance Frameworks and Tools
*
- Basic Qualifications
- Bachelor’s degree in computer science, Data Science, or a related field; master’s degree preferred.
- 5\+ years of professional and/or postgraduate academic research experience in software engineering.
- Preferred experience in SAP / Salesforce or Oracle programs.
- 4\+ year of experience designing and developing machine learning solutions.
- 3\+ years of experience with cloud native engineering, AWS, Azure, Google.
Generic Managerial Skills, If any
- Lead a team of junior developers
- Ability to translate requirements into understandable technical design.
- Task effort estimation and distribution among team members.
- Excellent communication skills, and stakeholder management.
- Ability to work with Cross functional teams and business users.
Base Salary Range : $160,000 to $200,000 Per Annum
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.
\#LI\-SV2
\#LI\-KUMARAN
Location
Seattle, WA
Job Function
TECHNOLOGY
Role
Technical Architect
Job Id
415578
Desired Skills
Artificial intelligence
Salary Range
$160,000\-$200,000 \[jobDescription.year]
Desired Candidate Profile
Qualifications : BACHELOR OF COMPUTER SCIENCE
Salary Context
This $160K-$200K range is above the median for AI Architect roles in our dataset (median: $180K across 25 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,824 AI roles we're tracking, AI Architect positions make up 1% of the market. At Tata Consultancy Services (TCS), this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $220,000 based on 92 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $160K to $200K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Tata Consultancy Services (TCS) AI Hiring
Tata Consultancy Services (TCS) has 31 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Architect, MLOps Engineer. Positions span Charlotte, NC, US, Houston, TX, US, Plano, TX, US. Compensation range: $100K - $286K.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% above the national median.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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