Lead ML Operations Engineer

Job Summary
 

Leads the architecture, design, and delivery of enterprise-scale AI, Generative AI, and Machine Learning solutions on AWS and Snowflake. Owns end-to-end solution development, including data ingestion, model/LLM integration, microservices-based architectures, agentic AI workflows, application development, and production deployment.

Drives the development of scalable, secure, and reusable platforms leveraging AWS services, Snowflake data capabilities, containerized deployments, and modern application frameworks such as Streamlit and ReactJS. Collaborates with cross-functional teams and senior stakeholders to translate business requirements into production-ready AI solutions, ensuring high performance, reliability, and governance.
 

Key Areas of Responsibility

• AI / GenAI Solution Architecture
  Design and lead end-to-end architecture for AI, ML, and GenAI solutions, including RAG, agent-based workflows, and predictive analytics systems.

• AWS-based Platform Development
  Architect and implement solutions using AWS services such as Amazon Bedrock, SageMaker, S3, Lambda, Step Functions, EventBridge, API Gateway, ECS/Fargate, ECR, EC2, SQS/SNS, CloudWatch, IAM, and Secrets Manager.

• Snowflake Data Integration
  Design and manage Snowflake-based data architectures, including integration with AWS S3, external stages, Snowpark Python, curated data layers, and governed data access for AI/GenAI workloads.

• Microservices-based Architecture
  Lead development of modular, scalable microservices for AI systems, including data services, model inference services, agent orchestration services, and UI/backend APIs.

• Streamlit and ReactJS Applications
  Drive development and deployment of AI-powered applications (dashboards, copilots, conversational UIs) using Streamlit and ReactJS, hosted on AWS (ECS/Fargate/EC2 with load balancing and API integration).

• Containerization & Orchestration
  Establish standards for containerization using Docker and deployment using Kubernetes/ECS/Fargate; ensure scalability, portability, and reliability of services.

• Multi-Agent / Agentic AI Systems
  Design and implement multi-agent architectures including planning, reasoning, summarization, retrieval, and orchestration agents; define workflows, guardrails, and human-in-the-loop mechanisms.

• MLOps and GenAIOps
  Define and govern CI/CD pipelines, model lifecycle management, prompt/version control, monitoring, drift detection, and production support processes.

• Performance, Monitoring & Reliability
  Ensure production systems meet SLAs for performance, availability, and accuracy through observability, logging, and proactive optimization.

• Security & Governance
  Implement secure design patterns including IAM, encryption, API security, data governance, and responsible AI practices.

• Technical Leadership & Stakeholder Management
  Lead cross-functional teams, review architecture and implementations, and present solutions, trade-offs, and recommendations to senior leadership.

• Innovation & Best Practices
  Drive adoption of emerging technologies in GenAI, MLOps, and distributed systems; establish reusable frameworks and enterprise standards.
   

Critical Experiences

• Education & Certifications
  Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field.
  Preferred certifications: AWS Machine Learning Specialty, AWS Solutions Architect, Kubernetes certifications. 5+ years of relevant experience.

• AI / ML / GenAI Expertise
  Strong experience designing and deploying production-grade ML and GenAI solutions, including LLM-based applications (RAG, agents, prompt engineering, evaluation frameworks).

• AWS Expertise
  Hands-on experience with AWS services including Bedrock, SageMaker, S3, Lambda, Step Functions, EventBridge, API Gateway, ECS/Fargate, ECR, EC2, SQS/SNS, CloudWatch, IAM, and security services; ability to design scalable and cost-efficient architectures.

• Snowflake Expertise
  Strong experience with Snowflake including SQL, Snowpark Python, external stages with S3, storage integrations, tasks, stored procedures, and performance optimization; exposure to Snowflake Cortex capabilities is a plus.

• Microservices & API Development
  Experience building scalable backend systems using REST APIs (FastAPI/Flask) and designing service-oriented architectures.

• Application Development
  Experience building AI-enabled applications using Streamlit and/or ReactJS and deploying them on AWS infrastructure.

• Containerization & Orchestration
  Strong knowledge of Docker and container-based deployments; working experience with Kubernetes concepts or AWS ECS/Fargate.

• MLOps / DevOps
  Experience in CI/CD, model deployment pipelines, monitoring, versioning, and production support for ML/GenAI systems.

• Data Engineering & Distributed Systems
  Experience with large-scale data processing (Spark, Hadoop, NoSQL) and building scalable data pipelines.

• Programming Skills
  Strong proficiency in Python and SQL; familiarity with Java/Scala and open-source ML frameworks.

• Leadership & Impact
  Proven ability to lead complex AI initiatives, mentor teams, influence stakeholders, and deliver scalable, business-impacting solutions.