Machine Learning Engineer (AWS AI/ML)

6 years

15 - 20 Lacs

Posted:2 days ago| Platform: Linkedin logo

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Work Mode

On-site

Job Type

Contractual

Job Description

  • 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).
Skills: aws,ml,bedrock

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