Machine Learning Engineer

Key Responsibilities: ● Design and implement AI product features. ● Maintain and optimize existing AI systems. ● Train, evaluate, deploy, and monitor ML models. ● Design ML pipelines for experiment, model, and feature management. ● Implement A/B testing and scalable model inferencing APIs. ● Optimize GPU architectures, parallel training, and fine-tune models for improved performance. ● 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: ● Collaborate with cross-functional teams to design and build scalable ML solutions. ● Implement state-of-the-art ML techniques, including NLP, Generative AI, RAG, and Transformer architectures. ● Deploy and monitor ML models for high performance and reliability. ● Innovate through research, staying ahead of industry trends. ● Build scalable data pipelines following best practices. ● Present key insights and drive decision-making.

Job Types: Full-time, Permanent

Pay: ₹3,000,000.00 - ₹5,000,000.00 per year

Experience:

• Total Work: 3 years (Preferred)
• LLM's: 3 years (Preferred)

Work Location: In person