Generative AI Engineer

Description :

Key Responsibilities :

Generative AI Development :

- Build, fine-tune, and deploy LLMs, Vision models, multimodal models, and agent-based workflows.

- Develop prompt engineering strategies, reusable prompt templates, and optimized dialogues for model performance.

- Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases.

- Experiment with open-source and proprietary LLMs (GPT, Llama, Mistral, Claude, Gemini, etc.).

Machine Learning Engineering :

- Train, evaluate, and optimize machine learning models using Python, PyTorch, TensorFlow, or similar frameworks.

- Perform model optimization, quantization, and latency improvements.

- Build scalable ML pipelines and model life cycle management (MLOps).

Cloud & Deployment :

- Deploy GenAI applications on AWS / Azure / GCP, using services such as :

1. AWS Sagemaker / Bedrock

2. Azure OpenAI

3. GCP Vertex AI

- Implement containerized AI solutions using Docker, Kubernetes, and CI/CD pipelines.

Data Engineering & Integration :

- Design and manage datasets, embeddings, feature stores, and vector indexes using Pinecone, Weaviate, FAISS, Chroma DB, etc.

- Integrate GenAI solutions with enterprise systems, APIs, and microservices.

Product & Collaboration :

- Work with product, UX, and engineering teams to conceptualize AI-driven features.

- Conduct PoCs, create architecture diagrams, and deliver end-to-end AI solutions.

- Stay updated with the latest AI research, tools, and best practices.

Required Skills & Experience :

Technical Skills :

- 3- 10+ years of experience in AI/ML, NLP, or software development (depending on role level).

- Strong hands-on experience with Python, ML frameworks, and LLM tooling.

- Experience with LangChain, LlamaIndex, Hugging Face, or similar ecosystems.

- Knowledge of vector databases and RAG architecture.

- Strong understanding of neural networks, transformers, and LLM architecture.

- Familiarity with MLOps, pipelines, versioning, and deployment.

- Experience with cloud technologies (AWS/Azure/GCP).

Soft Skills :

- Strong communication and documentation skills.

- Ability to work independently and in cross-functional teams.

- Problem-solving mindset and innovation-oriented thinking.

Preferred Qualifications :

- Experience fine-tuning or pretraining LLMs.

- Knowledge of multi-agent workflows (OpenAI Agents, CrewAI, AutoGen, LangGraph).

- Experience with multimodal AI (image, speech, video models).

- AI/ML certifications (AWS, Azure, Google, DeepLearning.AI).

- Exposure to responsible AI, bias evaluation, and model governance.