JD:
Experienced AI/ML Engineer with expertise in Machine Learning, Deep Learning, NLP,
and Generative AI. strong expertise in LLMs, Retrieval-Augmented Generation (RAG),
Agentic AI, and MLOps to develop scalable and production-ready AI solutions.
Key Responsibilities:
Develop & Optimize AI/ML Solutions: Build and deploy LLMs, RAG, Agentic AI,
and GenAI applications.
ML Pipeline Development: Implement and automate scalable ML pipelines using
MLflow, AWS SageMaker, Databricks, and PySpark.
MLOps Implementation: Establish CI/CD workflows, model versioning, monitoring,
and automated retraining.
Cloud &: Infrastructure: Leverage AWS AI/ML services (Sagemaker, Bedrock,
Lambda, Step Functions, ECS/EKS) for scalable AI solutions.
Data Engineering with PySpark: Optimize large-scale ETL workflows, data pipelines,
and distributed data processing.
LLMs & RAG Applications: Fine-tune and deploy LLMs integrated with vector
databases (FAISS, Pinecone, ChromaDB).
NLP & Deep Learning: Work with transformers, embeddings, multi-modal AI
models, and text processing frameworks. Orchestration & Containerization:
Deploy and manage AI workloads using Kubernetes (EKS), Docker, and CI/CD
pipelineSenior Data Engineer with special emphasis and experience of 10 to 15 years
on Artificial Intelligence and Machine Learning. Bachelor degree in computer science
Engineering, or related field.
Strong Hands on Experience on Python coding and all the python libraries. Manage
and direct processes and R&D (research and development) to meet the needs
of our AI strategy. Understand company and client challenges and how integrating AI
capabilities can help lead to solutions. As a Machine Learning Engineer, you will play
a crucial role in the development and implementation of cutting-edge artificial
intelligence products.
No of Experience:
10+ years' experience.