Machine Learning Engineer
2 weeks ago
Key Responsibilities
- Design, develop, and deploy ML models for Agentic AI use cases.
 - Work with AWS AI/ML ecosystem (SageMaker, Bedrock, Lambda, Step Functions, S3, DynamoDB, Kinesis).
 - Preprocess and engineer features from structured, unstructured, and streaming data.
 - Collaborate with data engineers to ensure high-quality, well-curated training datasets.
 - Implement LLM fine-tuning, embeddings, and retrieval-augmented generation (RAG) pipelines.
 - Evaluate and optimize models for accuracy, performance, scalability, and cost-efficiency.
 - Integrate models into production applications and APIs.
 - Work with MLOps teams to automate training, testing, deployment, and monitoring workflows.
 - Perform experimentation, A/B testing, and model validation to ensure reliability.
 - Document experiments, pipelines, and best practices for reproducibility.
 
Required Skills & Qualifications
- 3–6 years of experience in ML engineering (adjust based on seniority).
 - Strong programming skills in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
 - Solid understanding of ML lifecycle (data preprocessing, training, evaluation, deployment).
 - Experience with AWS services for ML (SageMaker, Lambda, ECS/EKS, Step Functions, Bedrock).
 - Familiarity with large language models (LLMs), NLP, and embeddings.
 - Strong knowledge of APIs and microservice deployment.
 - Experience with ML pipeline orchestration (Airflow, Kubeflow, MLflow, or similar).
 - Understanding of data versioning, experiment tracking, and model registry.
 - Proficiency in SQL/NoSQL databases and vector databases (Weaviate, Pinecone, FAISS).
 
Job Type: Full-time
Pay: ₹1,071, ₹2,583,079.16 per year
Work Location: Remote
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