AI Engineer

Infosys; Irving (TX); Presencial; USD 150.000 - 200.000; Jornada completa

Descripción de la vacante

Infosys is seeking a senior GenAI/ML Engineer to architect and deliver production-grade AI solutions for enterprise clients. You will lead discovery workshops, design architectures, and build end-to-end GenAI features with strong emphasis on data quality, security, and ROI.

You will collaborate with cross-functional teams to deploy scalable AI systems, evaluate models, and ensure responsible AI practices throughout the lifecycle.

Formación

• 6-12 years in software development, including 2+ years delivering ML/GenAI or data solutions.

Responsabilidades

• Advise clients on GenAI and agentic AI use cases, discovery workshops, and risk assessments.
• Build business cases with success metrics and value tracking.
• Evaluate GenAI models and design solution architecture for client problems.
• Design and prototype GenAI features focused on user value.
• Define evaluation methods using golden datasets and metrics.
• Optimize models for performance and cost, deploy AI solutions.
• Set production-readiness criteria with monitoring for drift.
• Design IDP solutions with OCR and extraction in GenAI/RAG.
• Mentor team members on GenAI design and evaluation.

Conocimientos

ML/GenAI experience

Python proficiency

LLM evaluation design

Model quality/latency trade-offs

Responsible AI & GenAI security

Agent frameworks & tool-calling

Cloud AI platforms

Data structures & algorithms

Client-facing experience

Prototyping to production

Educación

Bachelor's or Master's in CS/DS/Engineering

Herramientas

LangChain

LangGraph

Google ADK

CrewAI

AutoGen

AWS Bedrock

Vertex AI

Azure OpenAI

Descripción del empleo

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.