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