Machine Learning & Generative AI Engineer - LLM Models

Description :

We are looking for a highly skilled Machine Learning & GenAI Engineer with 45 years of hands-on
experience in building ML models. The ideal candidate should be capable of independently designing PoCs, implementing MLOps pipelines, integratingng LLMs and converting prototypes into scalable product on systems. This role is part of our AI Center of Excellence, focusing on innovation, rapid prototyping, and enterprise-ready solution development.

Requirement Description :

Total Experience :

- 4 to 5 years of hands-on experience in Machine Learning Engineering, AI Engineering, or MLOps.

Domain Experience :

- 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 : Bachelor's or Master's degree in Computer Science/Data Science/AI.

Mandatory Skills :

- Programming: Expert proficiency in Python (including libraries like NumPy, Pandas, 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 MLOps 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 :

- 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, FeatureStores

- Understanding of security, compliance & responsible AI prac>ces.