Cloud Support Engineer

Role Proficiency:

Act creatively to develop applications and select appropriate technical options optimizing application development maintenance and performance by employing design patterns and reusing proven solutions account for others' developmental activities

Outcomes:

1. Interpret the application/feature/component design to develop the same in accordance with specifications.
2. Code debug test document and communicate product/component/feature development stages.
3. Validate results with user representatives; integrates and commissions the overall solution
4. Select appropriate technical options for development such as reusing improving or reconfiguration of existing components or creating own solutions
5. Optimises efficiency cost and quality.
6. Influence and improve customer satisfaction
7. Set FAST goals for self/team; provide feedback to FAST goals of team members

Measures of Outcomes:

1. Adherence to engineering process and standards (coding standards)
2. Adherence to project schedule / timelines
3. Number of technical issues uncovered during the execution of the project
4. Number of defects in the code
5. Number of defects post delivery
6. Number of non compliance issues
7. On time completion of mandatory compliance trainings

Outputs Expected:

Code:

1. Code as per design
2. Follow coding standards
   templates and checklists
3. Review code - for team and peers

Documentation:

1. Create/review templates
   checklists
   guidelines
   standards for design/process/development
2. Create/review deliverable documents. Design documentation
   r and requirements
   test cases/results

Configure:

1. Define and govern configuration management plan
2. Ensure compliance from the team

Test:

1. Review and create unit test cases
   scenarios and execution
2. Review test plan created by testing team
3. Provide clarifications to the testing team

Domain relevance:

1. Advise Software Developers on design and development of features and components with a deep understanding of the business problem being addressed for the client.
2. Learn more about the customer domain identifying opportunities to provide valuable addition to customers
3. Complete relevant domain certifications

Manage Project:

1. Manage delivery of modules and/or manage user stories

Manage Defects:

1. Perform defect RCA and mitigation
2. Identify defect trends and take proactive measures to improve quality

Estimate:

1. Create and provide input for effort estimation for projects

Manage knowledge:

1. Consume and contribute to project related documents
   share point
   libraries and client universities
2. Review the reusable documents created by the team

Release:

1. Execute and monitor release process

Design:

1. Contribute to creation of design (HLD
   LLD
   SAD)/architecture for Applications/Features/Business Components/Data Models

Interface with Customer:

1. Clarify requirements and provide guidance to development team
2. Present design options to customers
3. Conduct product demos

Manage Team:

1. Set FAST goals and provide feedback
2. Understand aspirations of team members and provide guidance
   opportunities
   etc
3. Ensure team is engaged in project

Certifications:

1. Take relevant domain/technology certification

Skill Examples:

1. Explain and communicate the design / development to the customer
2. Perform and evaluate test results against product specifications
3. Break down complex problems into logical components
4. Develop user interfaces business software components
5. Use data models
6. Estimate time and effort required for developing / debugging features / components
7. Perform and evaluate test in the customer or target environment
8. Make quick decisions on technical/project related challenges
9. Manage a Team mentor and handle people related issues in team
10. Maintain high motivation levels and positive dynamics in the team.
11. Interface with other teams designers and other parallel practices
12. Set goals for self and team. Provide feedback to team members
13. Create and articulate impactful technical presentations
14. Follow high level of business etiquette in emails and other business communication
15. Drive conference calls with customers addressing customer questions
16. Proactively ask for and offer help
17. Ability to work under pressure determine dependencies risks facilitate planning; handling multiple tasks.
18. Build confidence with customers by meeting the deliverables on time with quality.
19. Estimate time and effort resources required for developing / debugging features / components
20. Make on appropriate utilization of Software / Hardware's.
21. Strong analytical and problem-solving abilities

Knowledge Examples:

• Appropriate software programs / modules
  
  
  
  1. Functional and technical designing
  2. Programming languages - proficient in multiple skill clusters
  3. DBMS
  4. Operating Systems and software platforms
  5. Software Development Life Cycle
  6. Agile - Scrum or Kanban Methods
  7. Integrated development environment (IDE)
  8. Rapid application development (RAD)
  9. Modelling technology and languages
  10. Interface definition languages (IDL)
  11. Knowledge of customer domain and deep understanding of sub domain where problem is solved
  
  
  
  
  Additional Comments:
  
  

  About the Team We are a group of software engineers and data scientists that lead innovation within the Business Services vertical - rapidly prototyping, A/B testing and shipping ML/AI features that solve real customer problems. We also drive internal productivity by building reusable platforms and developer tooling that speed experimentation, development and scale impact across teams. About the Role The ideal candidate will work on building intelligent systems and machine learning solutions while collaborating with cross-functional teams to deliver innovative AI-powered applications. Responsibilities * Design and develop maintainable machine learning models and AI-powered features using modern frameworks and techniques * LLM engineering: design prompt templates, context management strategies such as chunking, windowing, re ranking, and RAG pipelines; select embedding models and vector indices; tune retrieval quality and cost * Complete bug fixes and implement new features across various components of the ML/AI stack * Work closely with other development team members to understand product requirements and translate them into software designs * Document methodologies and follow best practices in machine learning engineering and model development * Conduct experiments and track effectiveness using A/B evaluations, error taxonomies, ablation studies, and cost/performance analyses * Communicate insights drawn from experiments and trade offs to stakeholders * Attend meetings with project teams and stakeholders to address technical questions and provide technical guidance * Partner with platform engineering team to package AI models and LLM services, establish inference SLAs, and integrate with API gateways and observability tools * Build and optimize pipelines for model training, evaluation, and deployment * Troubleshoot existing AI based systems, resolve issues with model performance, and optimize inference processes * Implement maintainable automated processes for model monitoring and continuous improvement Requirements Essential Qualifications * Hands-on experience with Large Language Models (LLMs) and practical implementation * Strong understanding of core Generative AI concepts including: * Token management and optimization * Embeddings and vector representations * Context window management and optimization * Model behavior and limitations * Expertise in prompt design and engineering for optimal model performance * Experience with evaluation methodologies for LLM outputs and model quality assessment * Proficient in Retrieval-Augmented Generation (RAG) architectures and implementation including document segmentation, retrieval quality tuning, re ranking, grounding, etc * Solid core Natural Language Processing (NLP) skills and understanding of NLP fundamentals such as NER, classification, summarization, and multilingual awareness * Advanced Python expertise with deep understanding of ML/AI libraries and frameworks * Understanding of test-driven development and model validation best practices * Strong oral and written communication skills * Ability and desire to learn new processes and technologies Preferred Qualifications * Fine-tuning experience with foundation models and domain adaptation techniques * Familiarity with the Azure cloud platform, including Azure OpenAI Service, Azure Machine Learning, Cognitive Services, and other related services * Experience with ML frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain * Knowledge of vector databases and semantic search technologies * Proficient in data manipulation and working with large-scale datasets * Understanding of MLOps practices and model lifecycle management * Experience with containerization (Docker) and orchestration tools * Familiarity with additional programming languages and data storage subsystems * Experience with version control systems (Git) and collaborative development workflows

  
  

LLM ,Token management,Vector representation ,Python