Key Responsibilities
- Design, develop, and deploy AI / ML models with a strong focus on Generative AI (LLMs, diffusion models, multimodal AI).
- Build, fine-tune, and evaluate large language models for domain-specific applications.
- Develop agentic AI systems (autonomous / assistive agents) capable of reasoning, planning, and tool use.
- Integrate AI / ML models into production systems, APIs, and applications.
- Conduct experiments, benchmark models, and optimize for scalability, efficiency, and cost.
- Write clean, modular, and well-documented code for ML pipelines and AI services.
- Implement best practices for model monitoring, observability, and troubleshooting in production.
- Contribute to research exploration, technical discussions, and knowledge sharing within the AI / ML team.
What We're Looking For
1–2 years of experience as an AI / ML Engineer.Strong hands-on expertise with Python and popular ML / AI frameworks ( PyTorch, TensorFlow, Hugging Face ).Solid understanding of Generative AI concepts (LLMs, embeddings, prompt engineering, RAG).Familiarity with agentic AI frameworks (LangChain, LlamaIndex, Haystack, AutoGen, etc.).Experience in building, fine-tuning, or deploying transformer-based models .Knowledge of vector databases (Pinecone, Weaviate, FAISS, Milvus).Understanding of cloud platforms (AWS, GCP, Azure) and containerization ( Docker, Kubernetes ).Strong foundation in data structures, algorithms, and ML fundamentals .Excellent problem-solving, debugging, and communication skills .Nice to Have
Experience with reinforcement learning, multi-agent systems, or AI reasoning frameworks .Knowledge of MLOps tools (MLflow, Weights & Biases, Kubeflow).Exposure to multimodal AI (text, image, audio, video).Familiarity with API development (Flask, FastAPI) for serving AI models.Contributions to open-source AI projects or published research .Skills Required
Algorithms, Tensorflow, Pytorch, Docker, Flask, Data Structures, Python, Aws, Gcp, FastAPI, Azure, Kubernetes