AI / ML Developer (Standard)

Job Description – AI Engineer (ML & GenAI)

About the Role

We are seeking a highly skilled AI Engineer with expertise in Machine Learning (ML) and Generative AI (GenAI) to design, build, and deploy intelligent solutions that drive business impact. The ideal candidate is a hands-on technologist who can work across the AI lifecycle—from data preprocessing and feature engineering to model development, fine-tuning, deployment, and monitoring.

Key Responsibilities

• AI/ML Development

o Design, develop, and optimize ML models for classification, regression, recommendation, forecasting, and anomaly detection.

o Implement GenAI solutions including LLMs, text-to-SQL, and conversational AI applications.

o Fine-tune and optimize pre-trained models (e.g., GPT, LLaMA, Falcon, Stable Diffusion).

o Perform feature engineering, hyperparameter tuning, and model evaluation.

• Data Engineering & Pipelines

o Build and maintain scalable data pipelines for training and inference.

o Work with structured and unstructured data

o Implement vector databases (e.g., Pinecone, Weaviate, FAISS) for retrieval-augmented generation (RAG).

• Model Deployment & MLOps

o Deploy models to production using Docker, Kubernetes, and CI/CD pipelines.

o Implement monitoring, model drift detection, and retraining workflows.

o Ensure scalability and low-latency inference using APIs and serving frameworks (e.g., TensorFlow Serving, TorchServe, FastAPI).

• Research & Innovation

o Stay updated on state-of-the-art ML and GenAI advancements.

o Experiment with new architectures and methods (transformers, diffusion models, reinforcement learning).

o Collaborate with cross-functional teams to translate business requirements into AI-driven solutions.

Required Skills & Experience

• Education & Background

o Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or related field.

o 4–8 years of hands-on experience in ML/AI engineering.

• Core Technical Skills

o Strong proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn).

o Experience with LLMs, transformers, embeddings, RAG pipelines.

o Proficiency in NLP, Computer Vision, and GenAI techniques.

o Proficient in LangChain, LlamaIndex, Hugging Face Transformers

o Familiarity with prompt engineering and fine-tuning LLMs.

o Strong understanding of algorithms, statistics, and optimization.

• MLOps & Cloud

o Experience with AWS or Azure AI/ML services.

o Hands-on with Docker, Kubernetes, CI/CD, and model deployment.

o Familiarity with MLflow, Kubeflow, or Vertex AI.

• Other Skills

o Strong problem-solving, debugging, and performance optimization skills.

o Ability to write clean, scalable, and production-grade code.

o Excellent collaboration and communication skills.

Nice-to-Have

• Exposure to reinforcement learning (RLHF) and multi-agent AI systems.
• Knowledge of ethical AI, bias detection, and model interpretability.