AI / ML Engineer

We are looking for a highly skilled Machine Learning and GenAI Engineer with 4-5 years of hands-on experience in building ML models, GenAI applications, and agent AI workflows. The ideal candidate should be capable of independently designing PoCs, implementing MLOps pipelines, integrating LLMs, and converting prototypes into scalable production systems.
Requirements

• Excellence, focusing on innovation, rapid prototyping, and enterprise-ready solution development.
• Total Experience: 4 to 5 years of hands-on experience in machine learning engineering, AI engineering, or MLOps.
• Proven experience in building and deploying at least 2 end-to-end ML/AI solutions in a production environment.
• Agentic/Gen AI Practical, hands-on experience with Generative AI and Agentic AI frameworks is highly preferred.
• Education: Bachelors or masters degree in computer science/data science/AI.
  

Mandatory Skills

• Programming: Expert proficiency in Python (including libraries like NumPy, Pandas, and Scikit-learn).

• ML/DL Frameworks: Hands-on experience with PyTorch or TensorFlow (and Keras).
• Generative AI: Experience with LLMs (e. g., OpenAI, Gemini, Llama) and core concepts like embeddings, tokenization, and fine-tuning.
• Agentic Frameworks: Proven experience with at least one Agent Orchestration Framework (e. g., LangChain, LangGraph, AutoGen).
• MLOps Tools: Practical experience with key ML Ops components: Docker, Kubernetes, and an
• MLOps platform/tool (e. g., MLflow, Kubeflow, DVC).
• Cloud Platform: Proficiency in deploying and managing AI/ML workloads on a major cloud platform (Databricks, AWS SageMaker, Google Cloud Vertex AI, or Azure ML).
• Databases: Strong knowledge of SQL and experience with vector databases (e. g., Pinecone, Weaviate).
• Familiarity with data engineering (Spark, SQL, ETL pipelines).
  

Good To Have Skills

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  Advanced MLOps: Experience with CI/CD tools (Jenkins, GitLab CI, GitHub Actions) and sophisticated monitoring tools (Prometheus, Grafana, Datadog).
• Big Data: Familiarity with distributed computing frameworks like Apache Spark.
• Front-End Integration: Experience with creating APIs (FastAPI, Flask) and integrating ML services with front-end applications.
• Other AI: Experience with computer vision or time-series analysis in a production setting.
  

Soft Skills

• Strong communication skills for CoE evangelism, cross-functional collaboration, and presenting POC results to stakeholders.
• Experience with RAG architectures.
• Exposure to Databricks Unity Catalog, DLT, Delta Live Tables, AutoML, and Feature Stores.
• Understanding of security, compliance & responsible AI practices.
  

This job was posted by Akshay Patil from Netscribes.