Position:Technical Lead – AI/ML & Generative AI
Location :Trivandrum, Kerala
Job Summary
We are seeking a highly skilled and experienced Technical Lead – AI/ML & Generative AI to lead the design, development, deployment, and scaling of enterprise-grade AI solutions.
The ideal candidate will have strong expertise in Machine Learning, Deep Learning, Generative AI, Agentic AI, MLOps, Data Engineering, and Cloud-Native AI Platforms.
The candidate will be responsible for leading AI initiatives, mentoring engineering teams, driving AI innovation, and delivering scalable AI products across business domains such as Financial Services, Lending, Customer Experience, Risk Analytics, Document Intelligence, and Intelligent Automation.
Key Responsibilities
AI Solution Architecture & Design
- Design scalable AI/ML architecture for enterprise applications.
- Lead AI platform strategy and technology roadmap.
- Define end-to-end AI solution architecture including:
- Data Ingestion
- Feature Engineering
- Model Training
- Model Deployment
- Model Monitoring
- Design AI-driven products using cloud-native architecture principles.
- Create reusable AI frameworks and accelerators.
Generative AI & Agentic AI
Lead implementation of:
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Multi-Agent Systems
- Agentic AI Platforms
- Prompt Engineering Frameworks
- AI Workflow Automation
Machine Learning & Deep Learning
Design and develop:
- Classification Models
- Regression Models
- Recommendation Engines
- Forecasting Models
- Anomaly Detection Systems
- Computer Vision Solutions
- NLP Applications
Hands-on expertise in:
- Scikit-Learn
- TensorFlow
- PyTorch
- XGBoost
- LightGBM
- Hugging Face
RAG & Knowledge Systems
Architect enterprise-grade RAG solutions:
- Vector Databases
- Semantic Search
- Hybrid Search
- Knowledge Graphs
- Embedding Pipelines
Experience with:
- LangChain
- LangGraph
- LlamaIndex
- Haystack
Vector Databases:
- Pinecone
- ChromaDB
- Weaviate
- Milvus
- FAISS
MLOps & AI Platform Engineering
Lead AI platform deployment and governance:
- Model Lifecycle Management
- Model Registry
- Model Monitoring
- Drift Detection
- Feature Stores
Tools:
- MLflow
- Kubeflow
- Airflow
- SageMaker
- Vertex AI
- Databricks
Implement:
- CI/CD for AI Models
- Automated Training Pipelines
- Continuous Model Deployment
Cloud & Infrastructure
Architect AI workloads on:
- AWS
- Azure
- OCI
- GCP
Services:
- AWS Bedrock
- SageMaker
- EC2
- EKS
- Lambda
- S3
Containerization:
- Docker
- Kubernetes
Deployment Models:
- On-Premise
- Hybrid Cloud
- Multi-Cloud
AI Governance, Security & Compliance
Establish AI governance framework:
- Responsible AI
- Explainable AI (XAI)
- Model Auditing
- AI Risk Management
Leadership & Team Management
- Lead AI/ML engineering team.
- Mentor Data Scientists and AI Engineers.
- Conduct architecture reviews and code reviews.
- Drive best practices in AI engineering.
- Collaborate with Product Owners and Business Teams.
- Own end-to-end AI solution delivery.