AI/ML Engineer

Role Overview :

Exp 3 to 10 yrs

Location : Delhi NCR

Must Have : python, LLM, RAG, AZURE, VertexAI, AgenticAI, Lang Chain, Machine Learning

Youll work closely with senior engineers to build applications leveraging LLMs (e.g., GPT-4, Claude, Gemini), diffusion models, and multimodal systems while adhering to ethical AI practices. This will be a hands-on individual contributor.

Key Responsibilities :

- Implement RAG (Retrieval-Augmented Generation) pipelines and optimize prompts for specific domains.

- Tooling & Integration LangChain, LlamaIndex, or Hugging Face. Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enterprise workflows.

- Prompt Engineering

Qualifications :

- Education : Bachelors/Masters in Computer Science, Data Science, or related field.

Technical Skills :

- Proficiency in Python and familiarity with AI/ML libraries (PyTorch, TensorFlow).

- Basic understanding of NLP (tokenization, attention mechanisms) and neural architectures (Transformers, GANs).

- Experience with cloud platforms (Vertex AI, Azure ML).

- Proficiency in prompt engineering tools : LangChain, DSPy, Guidance, or LMQL.

- Experience with AI deployment tools : FastAPI, Docker, or MLflow for model serving.

AI/GenAI Exposure and experience with at least two of the following :

- Hands-on projects with LLMs (fine-tuning, prompt engineering) or diffusion models.

- Familiarity with vector databases (Pinecone, Milvus) and orchestration tools.

- Fine-tuning/training LLMs (e.g., Llama 2, Mistral) using LoRA, QLoRA, or RLHF.

- Building RAG pipelines with vector DBs (Pinecone, Weaviate) and embedding models (BERT, OpenAI text-embedding).

- Developing applications with diffusion models (Stable Diffusion, DALL-E) or autoregressive architectures (GPT variants).

- Contributions to NLP projects (sentiment analysis, NER, text summarization) using libraries like spaCy or NLTK.

Preferred Qualifications Additions :

Certifications :