Machine Learning Engineer I

2 days ago


Bengaluru, Karnataka, India Skills Connect Full time ₹ 10,00,000 - ₹ 20,00,000 per year

Description :

- Collaborate with senior ML engineers to design and implement AI product features.

- Train, evaluate, and fine-tune ML and LLM models under guidance.

- Support the development and maintenance of scalable ML pipelines and APIs.

- Assist in data preprocessing, feature engineering, and model evaluation.

- Participate in code reviews, testing, and performance optimization.

- Help deploy and monitor ML models in production environments.

- Learn and apply best practices in LLMOps, DevOps, and cloud deployment.

- Contribute to documentation and internal knowledge-sharing.

- Deploy LLM solutions tailored to specific use cases.

- Ensure DevOps and LLMOps best practices using Kubernetes, Docker, and orchestration frameworks.

Technical Requirements :

- LLM & ML : Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama

- LLMOps : MLFlow, Langchain, Langgraph, LangFlow, Langfuse, LlamaIndex, SageMaker, AWS Bedrock, Azure AI

- Databases : MongoDB, PostgreSQL, Pinecone, ChromDB

- Cloud : AWS, Azure

- DevOps : Kubernetes, Docker

- Languages : Python, SQL, JavaScript

- Certifications (Bonus) : AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert

What You'll Do :

- Work on real-world Generative AI and NLP applications for the insurance domain.

- Build and deploy LLM-based pipelines using modern frameworks.

- Gain hands-on experience with cloud ML infrastructure.

- Learn how to manage end-to-end ML workflowsfrom experimentation to deployment.

- Collaborate with cross-functional teams (data, backend, product).

- Contribute to improving model accuracy, reliability, and scalability.

What You Need to Succeed :

- Bachelors or Masters degree in Computer Science, Machine Learning, or related field.

- experience in ML, data science, or related software engineering roles (internships count).

- Strong understanding of ML fundamentals, deep learning architectures, and data processing.

- Experience building or fine-tuning models using Python ML frameworks.



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