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Senior Full Stack AI Engineer
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Tata Consultancy Services Limited
Minneapolis, MN (In Person)
$110,000 Salary, Full-Time
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Job Description
Senior Full Stack AI Engineer Required Qualifications
- 8+ years of experience in software engineering, automation, or platform engineering roles.
- Proven hands-on experience implementing LLM based or agentic AI solutions in enterprise environments.
- Demonstrated expertise with Anthropic Cloud Code and advanced Claude services (including interpreter style workflows).
- Strong understanding of agent architectures, tool orchestration, and secure AI platform design.
- Experience working with investment, research, or Portfolio Management business users.
- Proficiency in Python and modern cloud native development patterns. Required Qualifications
- Experience building AI platforms or shared AI services in financial services or regulated environments.
- Familiarity with portfolio management, investment research, or quantitative analytics workflows.
- Experience integrating AI solutions with enterprise data platforms and analytics stacks.
- Exposure to Responsible AI frameworks, model governance, and audit requirements.
- Design agentic workflows and multi agent orchestration patterns using approved AgentCore frameworks and runtimes.
- Build AI capabilities that leverage:
- Tool calling
- Retrieval augmented generation (RAG)
- Structured data semantics
- Reusable prompt and skill Value Realization & Platform Adoption
- Demonstrate measurable impact on research productivity, decision velocity, and analytical depth.
- Support adoption via reference implementations, documentation, and enablement materials.
- Provide architectural guidance to teams building on AgentCore to prevent fragmentation and "shadow AI."Enterprise Platform Enablement
- Integrate AI automation into existing enterprise platforms (e.g., CI/CD pipelines, SDLC tooling, cloud platforms).
- Esta blish reusable AI components, frameworks, and guardrails for product and engineering teams.
- Enable adoption through reference architectures, implementation patterns, and developer enablement. Value Realization & Measurement
- Identify and quantify productivity, quality, and cycle time improvements driven by AI.
- Define KPIs and success metrics tied to SDLC efficiency, developer experience, and risk reduction.
- Support executive visibility into AI driven outcomes and maturity progress.