AI/ML Engineer

About the Role

We are looking for a skilled and motivated AI/ML Engineer to join our growing technology team. In this role, you will design, build, and deploy machine learning models and AI-driven solutions that solve real-world business challenges. You will collaborate closely with data scientists, product managers, and software engineers to take intelligent systems from prototype to production.

Key Responsibilities

1. Build, deploy, and maintain Machine Learning and AI models for production environments.
2. Develop end-to-end ML pipelines including data processing, training, evaluation, and deployment.
3. Work with structured and unstructured data for feature engineering and analysis.
4. Integrate AI/ML models with APIs and backend systems.
5. Monitor model performance and implement retraining strategies.
6. Collaborate with cross-functional teams to deliver AI-driven solutions.
7. Research and work on advanced AI technologies, NLP, LLMs, and modern ML frameworks.
8. Strong experience required in Python, TensorFlow, PyTorch, scikit-learn, SQL, Docker, and cloud platforms.
9. Knowledge of MLOps tools, REST APIs, Git, and containerized environments is required.
10. Experience with LangChain, RAG pipelines, vector databases, prompt engineering, and Agentic AI frameworks is a plus.

Required Skills

• 2–3 years of hands-on experience building and deploying ML/AI models in production.
• Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, or Keras.
• Solid understanding of supervised, unsupervised, and reinforcement learning algorithms.
• Experience with NLP techniques, text classification, embedding models, or LLM fine-tuning.
• Familiarity with MLOps tools — MLflow, Weights & Biases, DVC, or similar platforms.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP) for model hosting and inference.
• Strong grasp of data structures, SQL, and large-scale data processing (Pandas, Spark).
• Ability to work with REST APIs and containerized environments (Docker, Kubernetes basics).

Good understanding of software engineering principles and version control using Git

Nice to Have

• Experience with LLMs and prompt engineering (OpenAI, Anthropic Claude, Gemini, or open-source models).
• Exposure to RAG (Retrieval-Augmented Generation) pipelines and vector databases (FAISS, Pinecone, Weaviate).
• Knowledge of computer vision frameworks such as OpenCV, YOLO, or Detectron2.
• Contributions to open-source ML projects or published research papers.
• Familiarity with Agentic AI frameworks — Lang Chain, Lang Graph, AutoGen, or CrewAI.
• Experience with data annotation pipelines and RLHF workflows.