AIML Developer

Key Responsibilities

1. GenAI & ML Model Development

• Design and develop machine learning and Generative AI solutions for real-world business problems.
• Build applications using Large Language Models (LLMs) such as GPT, Llama, etc.
• Implement RAG (Retrieval-Augmented Generation) pipelines.
• Fine-tune or adapt pre-trained models where applicable.

2. Application Development (Python)

• Develop robust backend services and pipelines using Python.
• Work with libraries and frameworks such as:
• TensorFlow, PyTorch, Scikit-learn
• LangChain, LlamaIndex (preferred)
• Build APIs to expose ML/GenAI capabilities.

3. Data Engineering & Processing

• Work with structured and unstructured data.
• Build data pipelines, preprocessing, and feature engineering workflows.
• Ensure high-quality datasets for model training and validation.

4. Model Deployment & MLOps

• Deploy models into production using MLOps practices.
• Implement CI/CD for ML pipelines.
• Monitor model performance, drift, and retraining needs.
• Use tools like Docker, Kubernetes, MLflow (good to have).

5. System Design & Integration

• Design scalable AI/ML architectures integrated with enterprise systems.
• Collaborate with product and engineering teams to embed AI capabilities.
• Ensure security, compliance, and responsible AI practices.

6. Research & Innovation

• Stay updated with the latest advancements in GenAI and ML.
• Evaluate new tools, models, and frameworks.
• Drive innovation and proof-of-concepts (POCs).

Core Technical Skills

• Strong experience in:
• Python programming
• Machine Learning algorithms and concepts
• Generative AI / LLM-based applications
• Hands-on experience with:
• PyTorch / TensorFlow / Scikit-learn
• LangChain / LLM orchestration frameworks
• Experience in building and deploying REST APIs.

Cloud & DevOps

• Strong experience with AWS/Azure/GCP services for ML and application deployment.

Preferred Skills

• Experience with:
• RAG architectures and vector databases (Pinecone, FAISS, OpenSearch)
• Prompt engineering and prompt optimization
• Fine-tuning LLMs
• Knowledge of:
• Responsible AI, AI governance
• Model explainability tools

Soft Skills

• Strong analytical and problem-solving mindset.
• Excellent communication and stakeholder management.
• Ability to work independently and lead technical initiatives.
• Innovative thinking and curiosity for emerging technologies.

Educational Qualifications

• Bachelor’s or master's degree in computer science, Data Science, AI, or related field.

Nice to Have

• Certifications in:
• AWS (ML Specialty / Solutions Architect)
• Experience in domains like:
• Healthcare, Finance, Retail analytics