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:

• 3–5 years of professional 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.

• Bachelor’s degree in engineering or computers or AI

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

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