Educational Requirements

Bachelor of Engineering,BTech,BSc,BCA,MTech,MCA,MSc

Service Line

Data Analytics Unit

Responsibilities

- Solution Delivery Consulting Partner with client stakeholders to understand business goals, translate them into AI/ML and Generative AI use cases, and define success metrics.

- Contribute to solution design, effort estimation, and delivery planning for AI initiatives in a consulting environment.

- Communicate findings, trade-offs, and recommendations through clear documentation and presentations.

- Generative AI Development Build Python-based prototypes and production-ready components for Generative AI workflows (prompting, evaluation, and iteration).

- Develop and refine prompts, templates, and guardrails to improve response quality, safety, and consistency.

- Implement evaluation approaches to measure output quality (accuracy, relevance, hallucination checks) and drive continuous improvement.

- AI/ML Engineering Develop and maintain ML pipelines in Python for data preparation, training, inference, and monitoring.

- Perform model experimentation, feature engineering, and performance tuning aligned to business requirements.

- Collaborate with cross-functional teams to integrate AI services into applications and workflows.

- Minimum Qualifications: 35 years of qualified experience delivering Python-based solutions, including AI/ML or Generative AI components.





- Hands-on experience with Generative AI concepts and implementation (prompt engineering, evaluation, and iterative improvement).

- Working knowledge of AI/ML fundamentals (supervised/unsupervised learning, model validation, metrics).

- Strong Python programming skills with clean coding practices, testing, and debugging.

- Bachelors degree in engineering or computers or AI

Additional Responsibilities

- Preferred Qualifications: Experience delivering end-to-end AI/ML solutions in a client-facing or consulting setup, including requirement discovery and stakeholder management.

- Exposure to LLM application patterns such as RAG, embeddings, vector search, and tool/function calling.

- Familiarity with MLOps practices such as experiment tracking, model versioning, CI/CD for ML, and production monitoring.

- Experience with scalable data/ML platforms and workflows (e.g., Databricks-style notebook-to-production practices).

- Proven ability to balance rapid prototyping with production readiness, including performance, security, and reliability considerations.

- Good to have skills:RAG, Embeddings, Vector Databases, Prompt Engineering, MLOps

Technical and Professional Requirements

- Technology- >AI/ML, Python, Gen AI, Databricks

Preferred Skills

- Technology- >AI-AI Engineering- >AI/ML Solution Architecture and Design- >traditional ai ml

- Technology- >AI-Generative AI- >Prompt Engineering

📌 Generative AI Professional (Bengaluru)
🏢 Infosys
📍 Bengaluru