We are looking for a highly skilled Senior AI/ML Engineer to lead the design, development, deployment, and optimization of scalable AI solutions. The ideal candidate will have deep expertise in Generative AI, Large Language Models (LLMs), Machine Learning, Data Science, and MLOps. You will be responsible for building production-ready AI systems, driving innovation, mentoring junior team members, and collaborating with cross-functional teams to deliver impactful AI-powered solutions.

Key Responsibilities:

- Design and implement scalable AI/ML architectures and solutions.
- Lead the development of Generative AI, LLM, chatbot, and intelligent automation initiatives.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines.
- Develop, deploy, and monitor machine learning models in production environments.
- Own MLOps workflows, including CI/CD, model versioning, monitoring, and automation.
- Integrate AI models with enterprise applications and APIs.
- Optimize model performance, scalability, reliability, and cost efficiency.
- Mentor junior engineers and conduct technical reviews.
- Collaborate with Product, Engineering, and Data teams to define and execute AI roadmaps.
- Stay updated with the latest advancements in AI, GenAI, LLMs, and MLOps technologies.

Required Skills:

AI &

- Machine Learning:




- Robust expertise in Machine Learning, Deep Learning, and Data Science.
- Hands-on experience with Generative AI and Large Language Models (LLMs).
- Experience with Prompt Engineering, Fine-Tuning, and Model Evaluation.
- Strong understanding of NLP, embeddings, vector search, and semantic retrieval.

Frameworks &

- Tools:
- Python programming expertise.
- Experience with TensorFlow, PyTorch, Scikit-learn, LangChain, LlamaIndex, or similar frameworks.
- Experience working with OpenAI, Anthropic, Gemini, Claude, Llama, Mistral, or equivalent models.

RAG &

- Vector Databases:
- Experience building production-grade RAG systems.
- Hands-on experience with vector databases such as Pinecone, Weaviate, ChromaDB, Milvus, or FAISS.
- Knowledge of embedding models and retrieval strategies.

MLOps &

- Deployment:
- Strong experience with MLOps practices and model lifecycle management.
- Experience with Docker, Kubernetes, CI/CD pipelines, and model monitoring.
- Expertise in deploying AI/ML solutions in production environments.

Cloud &

- Infrastructure:
- Experience with AWS, Azure, or Google Cloud Platform (GCP).
- Understanding of scalable cloud-native architectures.
- Experience with REST APIs, microservices, and distributed systems.

📌 AI/ML Engineer - Generative AI/LLM (India)
🏢 White Force
📍 India