Experience
7 - 12 yrs
Job Location
Noida, India
Vacancy
1
Designation
Lead Machine Learning Engineer
Job Type
Not specified
Job Description
Gen AI - Lead
Location: Noida, UP, India
Company: Iris Software
- GenAI Application Development
- Build, optimize, and maintain generative AI applications using Python, FastAPI, and modern microservices patterns.
- Integrate AWS Bedrock foundation models and embeddings into enterprise workflows.
- Develop scalable, secure REST APIs to expose GenAI capabilities.
- AWS Cloud Engineering
- Design and implement serverless, event-driven AI workflows using AWS Lambda, Step Functions, API Gateway, SQS/SNS, and EventBridge.
- Build and optimize data persistence layers using AWS RDS (PostgreSQL/MySQL).
- AI/ML Pipeline Enablement
- Implement prompt engineering, model orchestration, and inference pipelines.
- Fine-tune and evaluate LLMs using Bedrock or other supported frameworks.
- Collaborate with Data Science teams to deploy custom models into AWS environments.
- System Architecture Best Practices
- Contribute to architecture designs for GenAI microservices and data flows.
- Ensure adherence to security, compliance, and cost-optimization best practices in AWS.
- Implement robust monitoring, logging, and observability for AI services.
- Collaboration Stakeholder Engagement
- Work with product managers, data engineers, cloud architects, and UX teams to translate requirements into scalable AI features.
- Document services, workflows, APIs, and operational runbooks.
- Innovation Continuous Improvement
- Evaluate new LLMs, embeddings, vector databases, and prompt orchestration tools.
- Explore advanced Python capabilities and emerging GenAI frameworks.
- Champion experimentation and rapid prototyping of AI-driven features.
Basic Qualifications
- Bachelors or Masters in Computer Science, Engineering, AI/ML, or related field.
- 8+ years of hands-on software engineering, with at least 3 years in AI/LLM-focused development.
- Expert-level proficiency in Python (async programming, advanced OOP, design patterns).
- Strong experience with FastAPI and REST API development.
- Hands-on expertise with AWS Lambda, Bedrock, Step Functions, RDS, S3, IAM, and CloudWatch.
- Proven experience building production-grade GenAI applications or LLM-integrated solutions.
- Familiarity with vector databases (FAISS, Pinecone, or AWS OpenSearch vectors).
- Solid understanding of CI/CD, DevOps, and GitOps workflows.
Preferred Qualifications
- Experience with LangChain or Bedrock Agents.
- Knowledge of RAG architecture and embedding pipelines.
- Experience with AWS SageMaker.
- Strong knowledge of containerized deployment (Docker).
- Experience optimizing high-performance Python applications.
- Exposure to MLOps and model evaluation metrics.
- Open-source contributions in AI or Python ecosystems.
Mandatory Competencies
- Data AI - GEN AI - Azure OpenAI Service
- Data AI - GEN AI - Retrieval Augmented Generation (RAG) / Graph RAG / Agentic AI Systems
- Data AI - GEN AI - Advanced GenAI Agentic Framework Concepts
- Data AI - GEN AI - Fine tuning Model Customization / AI Agents Tool Calling
- Data AI - GEN AI - Cloud Application Integration Deployment
- Data AI - GEN AI - Workflow Agentic Frameworks (LangChain / LangGraph)
- Data AI - GEN AI - Python
- Data AI - GEN AI - Amazon Bedrock
- Data AI - GEN AI - Azure AI Foundry
- Data AI - GEN AI - Prompt Engineering / Vector Databases
- Beh - Communication and collaboration
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