Experience:

• 3 to 5 years of software engineering experience
• 1 to 2 years in ML or AI-focused roles

Qualification:

• Bachelor’s degree in computer science, Engineering, Mathematics, or related field
• Equivalent demonstrable experience

Role Focus:

• Build, train, and evaluate ML models across full lifecycle
• Conduct exploratory data analysis, feature engineering, and model experimentation
• Support model validation, testing, and performance benchmarking
• Develop and maintain GenAI-powered applications including chatbots and document processing pipelines
• Implement prompt engineering, retrieval-augmented generation pipelines, and tool-augmented agents
• Evaluate new LLM capabilities and contribute to proof-of-concepts
• Build and maintain ML pipelines including data ingestion, preprocessing, training automation, and model serving
• Manage and monitor deployed models for performance degradation, drift, or anomalies
• Contribute to infrastructure-as-code for AI workloads across cloud environments
• Produce governance documentation such as model cards, data lineage records, and risk assessments
• Implement monitoring and logging frameworks for auditability and compliance
• Apply responsible AI checklists and flag bias, fairness, or privacy concerns
• Collaborate with data engineers, platform engineers, and business analysts to integrate AI outputs
• Participate in Agile ceremonies, sprint planning, and technical design discussions
• Write clean, well-documented, testable code and maintain technical documentation

Nice to have:

• Exposure to MLOps tools (MLflow, DVC, Weights & Biases)
• Experience with vector databases (Pinecone, Weaviate, ChromaDB, pgvector) for semantic search or RAG
• Familiarity with responsible AI principles, bias detection, or model explainability tools
• Cloud certifications (AWS, Azure, GCP) at associate level or above
• Experience in enterprise IT environment (ITSM, infrastructure, networking)

More skills:

ML libraries (scikit-learn, PyTorch, TensorFlow, pandas, NumPy), LLM APIs (OpenAI, Anthropic, HuggingFace), Orchestration frameworks (LangChain, LlamaIndex), Cloud platforms (AWS, Azure, GCP), Containerization (Docker), Version control (Git), CI/CD practices, Supervised and unsupervised learning, Model evaluation, Feature engineering