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## About the Role
We are looking for a Senior AI Python Engineer to lead the design, development, and deployment of intelligent systems that solve complex business problems. You will bridge the gap between research and production — building scalable AI pipelines, integrating large language models (LLMs), and mentoring junior engineers. If you are passionate about writing clean, efficient Python code and turning cutting‑edge AI research into reliable products, we want to hear from you.
## Key Responsibilities
- Design & Develop – Architect and implement production‑ready AI services, including data preprocessing, model training, inference pipelines, and APIs.
- Model Lifecycle Management – Lead end‑to‑end model development: from prototyping (Jupyter/notebooks) to versioning, testing, deployment, and monitoring (MLOps).
- LLM & Generative AI – Integrate and fine‑tune LLMs (e.g., GPT, Llama, Mistral) for tasks like RAG, summarization, classification, and agents.
- Performance Optimisation – Profile and optimise Python code, model inference latency, and memory usage (e.g., using ONNX, TensorRT, vLLM).
- Data Engineering – Work with large‑scale datasets: cleaning, feature engineering, and building efficient data loaders (Pandas, Polars, Spark, Dask).
- Collaboration – Partner with data scientists, ML researchers, and product managers to translate requirements into robust technical solutions.
- Mentorship – Guide and review code for junior engineers, promote best practices (testing, CI/CD, documentation), and contribute to technical roadmaps.
- Production Ownership – Deploy and maintain models on cloud infrastructure (AWS/GCP/Azure) using containers (Docker, Kubernetes) and serverless architectures.
## Required Qualifications
- 5+ years of professional Python development experience, with at least 3 years focused on AI/ML applications.
- Strong understanding of machine learning fundamentals (supervised/unsupervised, evaluation metrics, overfitting, cross‑validation).
- Deep experience with Python AI/ML stack:
- Frameworks: PyTorch, TensorFlow, or JAX
- Libraries: scikit‑learn, transformers (Hugging Face), LangChain, LlamaIndex
- Data: NumPy, Pandas, Polars, SQL
- Proven experience deploying models to production (e.g., FastAPI, Flask, Ray Serve, TorchServe, or BentoML).
- Solid grasp of software engineering best practices:
- Version control (Git), unit testing, integration testing
- Code reviews, CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)
- Docker, Kubernetes (or similar orchestration)
- Experience with cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML) and MLOps tools (MLflow, Weights & Biases, Kubeflow, TFX).
- Strong problem‑solving and communication skills – you can explain technical trade‑offs to non‑engineers.
## Nice‑to‑Have
- Experience with LLM fine‑tune (LoRA, QLoRA, PEFT) and quantization (GPTQ, AWQ).
- Knowledge of vector databases (Pinecone, Weaviate, Milvus, pgvector).
- Contributions to open‑source AI projects or research papers.
- Experience with real‑time inference
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