Apply Now: AI ML Engineers

2 weeks ago

Chennai, India Valuecoders Full time

Job Description Key Responsibilities - Design, build, and deploy ML and GenAI solutions across multiple use cases (e.g., LLM-based search, document summarization, predictive modeling). - Develop and maintain ML pipelines using MLflow, Databricks, and AWS Sagemaker. - Implement APIs and microservices for AI model inference using FastAPI and containerization strategies. - Work with large structured and unstructured datasets (CSV, JSON, PDFs, EMRs, clinical reports). - Optimize data processing using PySpark, SQL, and RDS (AWS). - Integrate foundation models and LLMs (e.g., OpenAI, Cohere, Claude, AWS Bedrock) into production workflows. - Monitor, retrain, and maintain models in production, ensuring high availability, reliability, and performance. - Collaborate with cross-functional teams (Data Engineering, Cloud, Product) to ensure scalable deployments. - Document processes, model cards, data lineage, and performance reports for governance and compliance. Required Skills Qualifications - 5+ years of hands-on experience in Machine Learning and AI engineering. - Strong Python programming skills with experience in FastAPI, PySpark, and SQL. - Hands-on experience with MLflow for experiment tracking, model versioning, and deployment. - Strong knowledge of Generative AI and LLMs: prompt engineering, fine-tuning, and model integration. - Experience working on Databricks (Delta Lake, ML runtime) and AWS Cloud services (S3, EC2, Lambda, RDS, Bedrock). - Familiarity with GitLab CI/CD, containerization (Docker), and cloud DevOps practices. - Deep understanding of machine learning workflows including data preprocessing, feature engineering, model selection, training, tuning, and deployment. Preferred / Nice to Have - Experience in Life Sciences or Healthcare domains: clinical data, RWD/RWE, regulatory AI, etc. - Familiarity with data privacy regulations (HIPAA, GDPR) or GxP practices in AI workflows. - Experience working with vector databases (e.g., FAISS, Pinecone) and retrieval-augmented generation (RAG). - Knowledge of performance monitoring, model drift detection, and responsible AI practices.

  • ▷ (Apply Now) Ai/ml Engineer

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