AI Engineer

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:

• Masters/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

Bachelors or Masters degree in informatics, computer science, or a related AI field.