Gen AI Engineer
About The Position
Infosys Topaz is an AI-first suite of services, solutions, and platforms that uses generative AI to accelerate business value. The role contributes to AI innovation and transformative projects, working with advanced technologies and industry experts in a learning-focused environment. In the assigned role of Data Science Consultant 2, the employee develops data preparation tasks and ensures data readiness for advanced modeling. They develop, test, deploy, and maintain predictive models and complex analytics solutions, including forecasting and LLM-based solutions, with attention to performance, reliability, scalability, governance, and compliance. The work includes using SAS and R/Python, building reusable customizations, standardizing code, documenting results, and contributing to analytics tools and multi-system integrations. The employee works with stakeholders to refine requirements and guide model implementation, contributes to knowledge sharing, training, thought leadership, intellectual property, and proofs of concept, and provides data-driven input to business planning. The position may require relocation and/or travel to the work or project location. Applicants must be authorized to work in the United States without employer-based visa sponsorship; Infosys does not provide immigration sponsorship for this role.
Requirements
- Python and hands-on experience building enterprise GenAI applications with LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks.
- Experience with RAG, vector databases, agentic workflows (including tool calling, memory, and multi-agent patterns), and prompt engineering.
- Experience working with Azure OpenAI, AWS Bedrock, OpenAI, Anthropic, or similar LLM platforms, and integrating with enterprise APIs, databases, and knowledge repositories.
- Experience building production APIs and microservices with FastAPI, Docker, and Kubernetes.
- Software engineering fundamentals, including system design, testing, CI/CD, and Git.
- Hands-on experience with AI coding assistants such as GitHub Copilot, Claude Code, or Cursor.
- Knowledge of LLMOps practices, including observability, tracing, evaluation, guardrails, cost governance, and model safety; evaluation tools may include RAGAS, DeepEval, or LangSmith.
- Experience conducting code reviews, driving technical decisions, and collaborating with product and platform teams.
- Bachelor’s degree or foreign equivalent from an accredited institution, or three years of progressive specialty experience in lieu of each year of education.
- Authorization to work in the United States for any employer without employer-based visa sponsorship.
Nice To Haves
- Experience with open-source LLMs such as Llama, Mistral, or Gemma and fine-tuning techniques such as LoRA, QLoRA, or PEFT.
- Familiarity with Model Context Protocol (MCP).
- Experience with multimodal AI, including vision-language, OCR, speech, or document intelligence.
- Front-end experience with React or TypeScript.
- Experience with DevOps or infrastructure-as-code tools such as GitHub Actions, Terraform, or Helm.
- Domain exposure in financial services, telecom, retail, or healthcare.
Responsibilities
- Develop data preparation tasks, identify patterns or anomalies, and ensure data readiness for advanced modeling.
- Develop models for complex use cases, including forecasting and LLM-based solutions, and refine algorithms to meet business needs.
- Test and optimize algorithms for performance, reliability, and scalability, and guide team members in best practices.
- Design, build, evaluate, deploy, and proactively maintain predictive models and data-driven analyses; improve model accuracy and reliability.
- Standardize code and contribute to knowledge management and documentation.
- Use tools such as SAS and R/Python to create reusable customizations for non-ML, ML, and deep learning algorithms, enhance analytics including LLMs, and create cost-effective solutions.
- Define analytics problems and execute visualization, analysis, and predictive modeling under guidance.
- Apply governance controls and quality measurement frameworks to mitigate risks and ensure compliance.
- Analyze performance trends, recommend improvements, and document discrepancies for escalation.
- Participate in stakeholder discussions to refine requirements, provide insights, and guide model implementation.
- Deploy complex analytics tools or multi-system integrations and validate deployment success.
- Help develop scripts or templates for repeat deployment tasks.
- Contribute to analytic solutions, intellectual property assets, training initiatives, thought leadership, papers, models, and proofs of concept.
- Deliver analytics training and contribute to training content.
- Provide input for segment- and unit-level business plans.
- Deliver scalable, high-quality analytics solutions aligned to business needs and support optimization, deployment, and performance improvement.
- Enable team growth through knowledge sharing, training, and standardization, and support business planning with data-driven insights.
Benefits
- Medical, dental, vision, and life insurance.
- Long-term and short-term disability insurance.
- Health and dependent care reimbursement accounts.
- Accident, critical illness, hospital indemnity, and legal insurance.
- 401(k) plan and contributions dependent on salary level.
- Paid holidays and paid time off.
- Competitive pay.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
