I
AI Engineer
Infosys
2 hours ago
Full-time
On-site
Irving, Texas, United States
6-12 years in software development, including 2+ years hands-on delivering ML/GenAI or data solutions. Bachelor's or master's degree in computer science, Data Science, Engineering, or a related field .
Locations: Irving, Texas / Jersey City, New Jersey / Tampa, Florida
Skills Required:
Has implemented at least one ML/GenAI use case in production or in a limited project with real users, not only proofs of concept (PoCs). Strong hands-on background in
AI, machine learning, deep learning, and statistical modeling Proficiency in Python. JL5: build rapid prototypes and deliver production grade code. JL6: additionally review design and code. Designed LLM evaluation with test sets, metrics, LLM-as-judge and review calibrations. Able to explain model and architecture trade-offs on quality, latency, token cost, and risk to technical and business audiences. Working knowledge of Responsible AI (RAI) and GenAI security - prompt injection, data leakage and PII, and tool permissions. Have built tool-calling or multi-agent workflows with at least one agent framework (LangGraph/LangChain, Google ADK, CrewAI, AutoGen, or other agent SDKs). Hands-on with at least one cloud AI platform – AWS Bedrock, Google Vertex AI, or Azure AI Foundry / Azure OpenAI. Good to have: Understanding of IDP concepts including OCR, document classification, key-value and table extraction, multimodal or vision-language models, and extraction accuracy evaluation Strong understanding of data structures, algorithms and AI project lifecycle Good to have: Experience building labelled or evaluation datasets with domain experts, including sampling and quality checks. Good to have: Client-facing experience on presenting solutions, running discovery workshops, prioritizing use cases by value, feasibility and risk, and recommending the right approach: GenAI, agents, or deterministic. Key responsibilities:
Advise clients on GenAI and agentic AI use cases, strategies, run discovery workshops, feasibility and risk assessments, recommend right approaches and provide feedback for product roadmap Build business case with success metrics, operational costs, effort estimation, target and value tracking (post go-live). Evaluate and recommend suitable GenAI models (based on quality, latency, cost and regulatory needs) and design solution architecture for client’s problem statement Design and prototype GenAI features and applications, focusing on user needs and value Assess client data and knowledge sources for quality, coverage, freshness, ownership, access rights and sensitivity Keep up with the latest advancements in AI technologies, including Gen AI and the latest features of popular tools (RAG) Collaborate with engineering and design teams to ensure successful product integration Collect, clean, and preprocess data using appropriate tools and libraries, ensuring compatibility with AI algorithms and frameworks Define how each solution is evaluated using golden datasets, metrics checked against human review, and adhering to release gates before go-live. Optimize and fine-tune models for performance, scalability and efficiency Implement and deploy AI solutions in production environments under different frameworks Set production-readiness criteria with monitoring (tracing, quality, latency and cost) to track feedback loops and catch drift. Design and implement Intelligent Document Processing (IDP) solutions covering document ingestion, classification, OCR and layout-aware extraction, validation, and human-in-the-loop review, using GenAI, LLM, and RAG approaches Advise clients on IDP use cases such as invoices, contracts, KYC, and claims, and define success metrics such as field-level accuracy and straight-through processing rate Ability to build applications and use appropriate prompting on generative AI models Strong experience in Natural Language Processing, Large Language Models, RAG, and vision-based approaches Design and build agentic AI solutions, including tool calling, multi-agent orchestration, and RAG pipelines, using Python and frameworks such as LangChain and Google ADK Assess the ethical implications and risks associated with GenAI product deployments, and design guardrails, grounding, safety, and other Responsible AI (RAI) controls Support pre-sales activities by providing GenAI expertise and contributing to product roadmaps Mentor team members on GenAI design and evaluation practice.
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Skills Required:
Has implemented at least one ML/GenAI use case in production or in a limited project with real users, not only proofs of concept (PoCs). Strong hands-on background in
AI, machine learning, deep learning, and statistical modeling Proficiency in Python. JL5: build rapid prototypes and deliver production grade code. JL6: additionally review design and code. Designed LLM evaluation with test sets, metrics, LLM-as-judge and review calibrations. Able to explain model and architecture trade-offs on quality, latency, token cost, and risk to technical and business audiences. Working knowledge of Responsible AI (RAI) and GenAI security - prompt injection, data leakage and PII, and tool permissions. Have built tool-calling or multi-agent workflows with at least one agent framework (LangGraph/LangChain, Google ADK, CrewAI, AutoGen, or other agent SDKs). Hands-on with at least one cloud AI platform – AWS Bedrock, Google Vertex AI, or Azure AI Foundry / Azure OpenAI. Good to have: Understanding of IDP concepts including OCR, document classification, key-value and table extraction, multimodal or vision-language models, and extraction accuracy evaluation Strong understanding of data structures, algorithms and AI project lifecycle Good to have: Experience building labelled or evaluation datasets with domain experts, including sampling and quality checks. Good to have: Client-facing experience on presenting solutions, running discovery workshops, prioritizing use cases by value, feasibility and risk, and recommending the right approach: GenAI, agents, or deterministic. Key responsibilities:
Advise clients on GenAI and agentic AI use cases, strategies, run discovery workshops, feasibility and risk assessments, recommend right approaches and provide feedback for product roadmap Build business case with success metrics, operational costs, effort estimation, target and value tracking (post go-live). Evaluate and recommend suitable GenAI models (based on quality, latency, cost and regulatory needs) and design solution architecture for client’s problem statement Design and prototype GenAI features and applications, focusing on user needs and value Assess client data and knowledge sources for quality, coverage, freshness, ownership, access rights and sensitivity Keep up with the latest advancements in AI technologies, including Gen AI and the latest features of popular tools (RAG) Collaborate with engineering and design teams to ensure successful product integration Collect, clean, and preprocess data using appropriate tools and libraries, ensuring compatibility with AI algorithms and frameworks Define how each solution is evaluated using golden datasets, metrics checked against human review, and adhering to release gates before go-live. Optimize and fine-tune models for performance, scalability and efficiency Implement and deploy AI solutions in production environments under different frameworks Set production-readiness criteria with monitoring (tracing, quality, latency and cost) to track feedback loops and catch drift. Design and implement Intelligent Document Processing (IDP) solutions covering document ingestion, classification, OCR and layout-aware extraction, validation, and human-in-the-loop review, using GenAI, LLM, and RAG approaches Advise clients on IDP use cases such as invoices, contracts, KYC, and claims, and define success metrics such as field-level accuracy and straight-through processing rate Ability to build applications and use appropriate prompting on generative AI models Strong experience in Natural Language Processing, Large Language Models, RAG, and vision-based approaches Design and build agentic AI solutions, including tool calling, multi-agent orchestration, and RAG pipelines, using Python and frameworks such as LangChain and Google ADK Assess the ethical implications and risks associated with GenAI product deployments, and design guardrails, grounding, safety, and other Responsible AI (RAI) controls Support pre-sales activities by providing GenAI expertise and contributing to product roadmaps Mentor team members on GenAI design and evaluation practice.
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