Accountabilities
- AI Solution Architecture & Implementation
- Architect production-ready AI solutions integrating generative models with enterprise systems
- Design intelligent workflows combining generative AI, computer vision, and speech recognition
- Build hybrid architectures merging rule-based logic with LLMs
- Develop scalable Agentic AI implementations using AWS Bedrock and Langgraph platforms
- Create data pipelines, feature stores, and model serving architectures
- Advanced LLM Integrations
- Implement advanced prompt engineering: chain-of-thought, zero-shot/few-shot learning, constitutional AI
- Build sophisticated RAG systems with vector databases
- Design agentic AI using function calling, tool use, and autonomous frameworks (LangChain, LlamaIndex)
- Deploy custom generative applications via Gen AI Platforms and Assistants API
- Develop multi-modal solutions: GPT for (vision), DALL-E (images), Whisper (speech), Codex (code)
- MLOps & Production Deployment
- Deploy high throughput inference systems with auto-scaling on SageMaker Endpoints
- Implement CI/CD pipelines for AI using SageMaker Pipelines and Databricks Workflows
- Build monitoring systems: model drift detection, performance analytics, cost optimization
- Ensure AI security: prompt injection prevention, adversarial robustness, PII protection, Data leakage
- Establish model governance: version control, audit trails, compliance frameworks
- Technical Leadership & Strategy
- Lead AI transformation initiatives with clear KPIs and success metrics
- Mentor engineers and data scientists on AI best practices and architectural patterns
- Research and pilot emerging AI technologies and frameworks
- Develop AI governance frameworks addressing ethics, bias mitigation, and responsible AI
- Create ROI models and business cases for AI investments
- Create, execute and enforce solutions blueprint
Knowledge, experience & capabilities
Required:
- Master's/PhD in Computer Science (AI/ML specialization) or Data Science or related field
- 7+ years enterprise software development, solution engineering, or AI/ML consulting
- 3+ years production experience with Large Language Models and generative AI
- 2+ years hands-on experience with AWS Bedrock (training, deployment, pipelines, feature store)
- 2+ years hands-on experience with Langgraph, LangChain, Bedrock or Agentic AI frameworks
- Proven track record delivering complex AI projects from conception to production
Preferred:
- AWS Certified Solutions Architect Professional or Machine Learning Specialty
- OpenAI API certification or equivalent demonstrated expertise
Agentic AI Technical Expertise -
- Agent Design Patterns: Expertise in architecting multi-agent systems with clear role definitions, communication protocols, and coordination strategies; implementing ReAct (Reasoning + Acting), Plan-and-Execute, and Reflection patterns; and designing stateful agent workflows using LangGraph with checkpointing, error handling, and fallback mechanisms.
- Knowledge & Context Management: Proficient in designing RAG architectures for agent knowledge access, including chunking strategies, embedding selection, retrieval optimization, and context window management; implementing agent memory systems (short-term, long-term, semantic, and episodic memory); and creating knowledge base governance for version control, access policies, and content validation.
- Agent Observability & Governance: Skilled in establishing monitoring and evaluation frameworks for agent performance, accuracy, latency, and cost; implementing responsible AI controls including bias detection, hallucination mitigation, PII protection, and content filtering; and creating feedback loops for continuous agent improvement and human-in-the-loop validation.
Business & Leadership Skills
- Translate complex AI concepts into business value for C-level executives
- Lead cross-functional teams (3-8 engineers, architects)
- Conduct architecture reviews and drive technical decision-making
- Facilitate workshops, design sprints, and requirements gathering
- Build trusted advisor relationships with stakeholders
- Develop business cases with ROI projections and risk assessments.
- 7+ years of hands-on experience in AI/ML engineering or related roles
- Proven track record of deploying models to production environments
- Proficiency in Python
- Excelent understanding of AWS cloud architecture including Sagemaker and Bedrock
- Ability to use identify and re-use GitHub projects to solve business problems
- Experience working with cross-functional teams and translating business needs into technical solutions
- Demonstrated ability to manage multiple projects and deliver results in agile environments