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:
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
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
