
Machine Learning Engineer
1 day 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 Types: Full-time, Permanent
Benefits:
- Flexible schedule
- Health insurance
- Provident Fund
- Work from home
Application Question(s):
- How many years of hands-on experience do you have with Python ML frameworks (PyTorch, TensorFlow, Scikit-learn)?
- Do you have experience deploying ML models on AWS SageMaker / ECS / EKS?
Experience:
- Machine learning: 3 years (Required)
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