Sr Engineer AI

1.     Responsible for designing, building, and maintaining scalable AI solutions

2.     Develop and implement advanced Generative AI solutions (LLMs, embeddings, retrieval techniques, prompt engineering)

3.     Ensure that data architectures and infrastructure can scale seamlessly as the data volume and complexity grow

4.     Lead, mentor, and develop a team of AI Engineers, fostering a collaborative and inclusive team environment

5.     Identify and address skill gaps, and provide opportunities for professional development

6.     Coordinate with stakeholders to gather requirements, set priorities, and define project timelines

7.     Ensure projects align with overall business objectives and data strategy

8.     Ensure data quality, integrity, and security across all data engineering projects

9.     Identify opportunities for process improvements and drive initiatives to enhance the efficiency and effectiveness of data operations

10.Ability to build/drive reusable frameworks that can drive efficiency of the overall data system

Manages conversation with the client stakeholders to understand the requirement and translate it into technical outcomes Required Skills (Must have) Tech:

1. Experience of 4.5-7 years in development and deployment of scalable AI/ML solutions
2. Has strong execution knowledge of Data Modeling, Databases in general (SQL and NoSQL), software development lifecycle and practices, unit testing, functional programming, etc
3. Develop and implement advanced Generative AI solutions (LLMs, embeddings, retrieval techniques, prompt engineering)
4. Design and optimize Retrieval-Augmented Generation (RAG) solutions
5. Manage Databricks workflows, including job and cluster creation also Databricks API
6. Apply data structures and algorithms knowledge, including multiprocessing and optimization techniques
7. Utilize Python libraries (Pandas, Numpy, FastAPI) for data processing and API development
8. Perform SQL optimization and database architecture design (schema creation, normalization, functions, triggers)
9. Deploy and orchestrate AI models using Docker and Kubernetes
10. Collaborate using GitHub for version control and team collaboration
11. Work with cloud platforms for AI solution deployment and management (Azure, GCP, AWS)
12. Utilize PySpark for data processing (optional)
13. Basic understanding of CI/CD pipelines and deployment process

Non-Tech:

1. Strong problem-solving skills with an ability to assess the financial impact of decisions, both in running the delivery team and delivering solutions to clients
2. Proficient in written and verbal communication and able to hold conversations with mid-management-level clients
3. Ability to recognize pragmatic alternatives to a perfect solution and gain team buy-in to pursue them, balancing time priorities with potential business impact
4. Strong people skills, including conflict resolution, empathy, communication, listening, and negotiation
5. Shows proficiency in providing technical guidance and leadership, and mentors the delivery tea
6. Self-driven with a strong sense of ownership