Automation and AI Solutions Lead

Thomson Reuters

. Engage internal SMEs to understand current processes and as-is workflows and translate them into buildable AI requirements .

Posted 10/9/2026full-timeHyderabad • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and scaling AI solutions using AWS AgentCore and LangGraph, with a strong focus on production GenAI/LLM experience. Proficient in translating complex architectural designs into actionable implementation plans while ensuring effective stakeholder communication and technical decision-making.

Highest-signal resume keywords

AWS AgentCoreLangGraph Multi-Agent State MachinesProduction GenAI/LLM ExperienceExpert-Level PythonAWS ML Specialty Certification

ATS Keywords

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Hard Skills

SQL ProficiencyREST API DevelopmentAdvanced RAG ExperienceDocument IntelligenceLoRA/QLoRA Fine-TuningMCP/A2A Server ImplementationNL-to-SQLLangSmith or LangFuseDebugging Production FailuresAI Coding Tools

Soft Skills

Stakeholder CommunicationTechnical Decision-MakingGuiding DevelopersIssue EscalationSolution Walkthroughs

Tools & Technologies

Copilot StudioPower AutomateClaude CodeGitHub CopilotCursorClineAWS BedrockHITLGolden Test SetsAutomated Regression Pipelines

Certifications & Qualifications

AWS ML SpecialtyAzure AI EngineerGCP ML Engineer

Industry Keywords

AI SolutionsProduction ImplementationPerformance MonitoringCost ManagementModel DeploymentHybrid SearchRetrieval EvaluationCheckpointingParallel ExecutionAgent Tracing

Tech Stack

Tools & technologies

AWSAzureGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Engage internal SMEs to understand current processes and as-is workflows and translate them into buildable AI requirements
Demo working solutions to internal stakeholders and business leadership
Support the Architect during feasibility and POC phases through rapid builds and fit validation
Maintain stakeholder communication through progress updates, issue escalation, and solution walkthroughs
Co-invest with the Architect in upfront solution design
Translate architecture into implementation plans and assignable workstreams for Developers and Associates
Own day-to-day technical decisions during builds
Identify architectural risks early and escalate them before build or production problems occur
Build complex solution components end-to-end on AWS AgentCore and LangGraph
Scale solutions from POC to production, including performance, reliability, monitoring, and cost management
Debug production failures involving hallucination patterns, retrieval degradation, agent loops, and latency regressions
Use AI coding tools to accelerate delivery while owning all generated code
Build low-code automations with Copilot Studio and Power Automate when appropriate
Guide Developers and Associates through implementation, code reviews, and debugging
Ensure effective use of AI tools without sacrificing code understanding

Requirements

What you’ll need

5–8 years of total experience
2–4 years of production GenAI/LLM experience
12–24 months of multi-agent LangGraph experience
3+ systems owned post-deployment
Expertise in LangGraph multi-agent state machines, parallel execution, checkpointing, and HITL
AWS Bedrock experience with model deployment, knowledge bases, and guardrails
SQL proficiency, NL-to-SQL, and LLM-powered query layers; Snowflake is a plus
REST API development, MCP awareness, and enterprise system connectors
Advanced RAG experience including hybrid search, re-ranking, query reformulation, and retrieval evaluation
RAGAS or TruLens, golden test sets, and automated regression pipelines
LangSmith or LangFuse for agent tracing and production debugging
Expert-level Python, including design patterns, async, testing, and CI/CD
Production-scale experience with OpenAI, Claude, Gemini, and Llama and understanding of model trade-offs
Working knowledge of Copilot Studio and Power Automate
Experience with Claude Code, GitHub Copilot, Cursor, or Cline; ability to own and fix generated code
Ability to demo AI solutions to business stakeholders and translate SME knowledge into requirements
Hands-on experience with all AWS AgentCore services
Production implementation of MCP/A2A server and client
Document intelligence, OCR, and layout-aware chunking
LoRA/QLoRA fine-tuning for domain adaptation
Certifications: AWS ML Specialty, Azure AI Engineer, or GCP ML Engineer

Benefits

Comp & perks

Flexible hybrid working environment with 2–3 days a week in the office depending on the role
Work from anywhere for up to 8 weeks per year
Flexible vacation
Two company-wide Mental Health Days off
Access to the Headspace app
Retirement savings
Tuition reimbursement
Employee incentive programs
Resources for mental, physical, and financial wellbeing
Two paid volunteer days off annually
Opportunities to get involved with pro-bono consulting projects and ESG initiatives
Career development, continuous learning, and skills development through Grow My Way programming
Flexible work arrangements and work-life balance support through Flex My Way policies