Lead Assistant Manager - Lead Software Engineer

Candidate will be a part of the Advanced Analytics team within IMU - HLS and SGU – Domain Platforms contribute to analytics projects for Payers, Providers and Life Sciences organizations. Candidate is expected to amalgamate cutting-edge approaches with domain expertise to enhance client's business needs. Experience in managing delivery of complex analysis driven consulting type of projects or advanced analytics driven AI/ML projects will add value to the candidature.

• Skilled AI Developer to design and deploy AI solutions using LLMs.
  
  
  
  • Build scalable gen AI applications involving cloud based databricks environment.
  
  
  • Key Responsibilities: Data Engineering, RAG architecture, Cloud integration, API development and integration, Performance Tuning (Optimize model inference latency, throughput and compute costs.
  
  
  • Lead project delivery individually or along with other members by - gathering requirements, aligning on scope, designing the approach, generating a plan of action, executing delivery and managing stakeholder (client/internal) expectations
  
  
  • Experience/aptitude for developing solutions from scratch and translating analytics outputs into tangible business ready solutions
  
  
  • Facilitate data enabled decision making for customers by developing & communicating qualitative/quantitative analytics 
  
  
  • Intermediate to expert level of proficiency in coding with Python/R programming for data science
  
  
  • Good to have working experience on one of the cloud platforms (AWS, Azure, GCP) 
  
  
  • Good to have knowledge of US Healthcare industry (Data sources, Stakeholders etc.)
  
  
  • Ability to work with Clients, SMEs & other stakeholders to successfully understand and contribute in designing project/solution approach
  
  
  • Out-of-box thinking, being a team player and passion for solving complex problems are some key attributes expected
  
  
  • Conduct all job functions and responsibilities in accordance with all company Compliance, Information Security and Regulatory policies, procedures and programs.
  
  

• Skilled AI Developer to design and deploy AI solutions using LLMs.
  
  
  
  • Build scalable gen AI applications involving cloud based databricks environment.
  
  
  • Key Responsibilities: Data Engineering, RAG architecture, Cloud integration, API development and integration, Performance Tuning (Optimize model inference latency, throughput and compute costs.
  
  
  • Lead project delivery individually or along with other members by - gathering requirements, aligning on scope, designing the approach, generating a plan of action, executing delivery and managing stakeholder (client/internal) expectations
  
  
  • Experience/aptitude for developing solutions from scratch and translating analytics outputs into tangible business ready solutions
  
  
  • Facilitate data enabled decision making for customers by developing & communicating qualitative/quantitative analytics 
  
  
  • Intermediate to expert level of proficiency in coding with Python/R programming for data science
  
  
  • Good to have working experience on one of the cloud platforms (AWS, Azure, GCP) 
  
  
  • Good to have knowledge of US Healthcare industry (Data sources, Stakeholders etc.)
  
  
  • Ability to work with Clients, SMEs & other stakeholders to successfully understand and contribute in designing project/solution approach
  
  
  • Out-of-box thinking, being a team player and passion for solving complex problems are some key attributes expected
  
  
  • Conduct all job functions and responsibilities in accordance with all company Compliance, Information Security and Regulatory policies, procedures and programs.
  
  

Bachelor's degree in engineering, MCA or a related field. 3-6 years of experience in application development.

Skilled AI Developer to design and deploy AI solutions using LLMs. Build scalable gen AI applications involving cloud based databricks environment. Key Responsibilities: Data Engineering, RAG architecture, Cloud integration, API development and integration, Performance Tuning (Optimize model inference latency, throughput and compute costs.