What were looking for :

Join a lean, high-velocity AI team building GenAI infrastructure that powers enterprise workflows at scale.

You'll ship production-grade LLM solutions, run experiments, and work directly with the Lead Architect AI.

What You'll Build :

- LLM-powered solutions Design and deploy models that solve real enterprise workflow challenges.

- Multi-provider integrations Work with OpenAI, Claude, and open-source models (LLaMA, Mistral, Falcon).

- Advanced prompting & RAG pipelines Implement prompt engineering, context management, and retrieval-augmented generation.

- Fine-tuning & evaluation loops Curate datasets, tune models, and build reproducible evaluation frameworks.

- Production ML infrastructure Build Python pipelines using Hugging Face (Transformers, PEFT, Datasets), LangChain, and LlamaIndex.

- Performance monitoring Define metrics, track model drift, and maintain dashboards that ensure reliability.

Roles & Responsibilities :

- 3-5 Years Experience with Strong Python fluency for model training, evaluation, and API integration.

- CV & NLP fundamentals experience with image processing, text parsing, and embedding generation.

- LLM expertise hands-on with fine-tuning, prompt engineering, and context optimization.

- Modern tooling comfortable with Hugging Face ecosystem, LangChain/LlamaIndex, and vector databases (FAISS, Pinecone, Weaviate).

- API integration experience worked with LLM APIs (OpenAI, Claude, Cohere) and deployed small models to production.

- Rigorous experimentation dataset curation, benchmark design, and systematic eval loops.

- Enterprise awareness understands security, versioning, and scalability requirements.

Bonus Points :

- Backend development exposure (FastAPI, Node.js) for seamless model-service integration.

- Containerization & GPU infrastructure (Docker, Kubernetes).

- AWS AI services (SageMaker, Lambda, ECS/EKS).

- MLOps practices (CI/CD pipelines, model monitoring, version control).

Why This Role :

- Solve real problems Start with workflow friction, not models.

- Build GenAI for global field operations.

- Ship fast Prototype, measure, iterate.

- Deploy production code that scales.

- Build the category Define how AI transforms field service.

- Startup velocity, enterprise reach.