Senior Engineer Agentic AI

Role:
Senior Engineer - Agentic AI

Location:

Any US Location - must be willing to travel per business need

Job Description:

Preface The Agentic AI Engineer is a hands-on development role at TCS (Americas) specializing in building and deploying AI agent solutions for clients. As businesses shift toward agentic AI-autonomous systems that execute tasks independently-roles like "AI Agent Engineer" have emerged. In this client-facing consulting position, you'll work in a hybrid environment, delivering cutting-edge AI agents that blend large language models, custom prompts, data sources, and business logic.

Projects can range from financial chatbots to manufacturing optimizers, requiring advanced prompt engineering, Retrieval-Augmented Generation (RAG), and strong software skills.

What You Would Be Doing

* Develop AI Agents & Applications:
Code the core logic for AI agents, whether standalone or in multi-agent systems, enabling them to answer questions, generate content, or execute transactions.

* Coding with LLMs and Tools:
Use Python or similar languages to integrate large language models (LLMs) and external tools (e.g., APIs, web search, databases).

* Prompt Engineering & Optimization:
Craft, refine, and test prompts to guide agent behavior, including fallback strategies for uncertainty.

* Implement RAG for Knowledge:
Connect AI agents to vector databases or search indices to ground outputs in up-to-date, domain-specific information.

* System Integration & APIs:
Integrate AI agents with external systems (e.g., travel booking APIs, payment gateways), handling formatting, RESTful calls, and data responses as needed.

* Testing and Iteration:
Simulate agent behavior, identify and fix failure modes, and tune prompts and code for high-quality results.

* Deploy AI Solutions:
Package and deploy agent applications (Docker, cloud), ensuring scalability and proper configuration.

* Collaboration & Agile Delivery:
Work with AI Architects, Data Engineers, and UX Developers in agile teams, contributing to sprints and client demos.

* Industry-Specific Customization:
Tailor solutions for each industry, adapting compliance, personalization, and integration as needed.

* Adhere to AI Ethics & Safety:
Implement guardrails, content moderation, and privacy measures, following TCS's responsible AI guidelines.

What Skills Are Expected

* Programming & Software Engineering:
Expertise in Python (and optionally Java, JavaScript, or C#), unit testing, and version control (Git).

* AI/ML Knowledge:
Solid grasp of machine learning and AI concepts, model behavior, and experience with NLP or chatbots.

* Prompt Engineering:
Experience crafting and iterating prompts, including few-shot examples and output formatting techniques.

* RAG and Data Handling:
Familiarity with embedding models, vector databases, and unstructured data processing.

* API and Integration

Skills:

Building and consuming RESTful APIs, microservices, and handling JSON/XML data formats.

* Data Structures & Algorithms:
Knowledge of lists, dictionaries, trees/graphs, and their application in efficient agent design.

* Debugging & Problem-Solving:
Strong troubleshooting abilities to distinguish between model and code issues.

* Agile and Collaborative Mindset:
Comfortable working in sprints, collaborating across teams, and communicating technical needs.

* Domain Adap tability:
Ability to quickly learn new industry concepts for tailored agent solutions.

* Attention to Detail & Quality:
Consideration of edge cases, proper data handling, and thorough testing.

* Ethical Awareness:
Recognize bias, confidentiality issues, and flag questionable requests.

Key Technology Capabilities

* Languages & Frameworks:
Mastery of Python for AI/ML, with exposure to JavaScript/Type Script, FastAPI, or Flask for APIs.

* AI/ML Tools:

Experience with AI model APIs (OpenAI, Azure OpenAI), and ML frameworks like PyTorch or Tensor Flow.

* Agent Development Libraries:
Hands-on with Lang Chain or similar frameworks for prompt management and agent logic.

* Databases & Data Access:
Working with SQL, No

SQL, and vector databases (e.g., Pinecone, Weaviate) for data retrieval.

* Dev Ops & Deployment:
Familiarity with Docker, CI/CD, and cloud deployment (AWS, Azure, GCP, Lambda/Functions).

* Version Control &

Collaboration:

Proficient with Git and Dev Ops platforms (Git Hub, Git Lab, Bitbucket).

* Testing Tools:
PyTest, Postman, and AI evaluation methods.

* Cloud & Services:
Practical knowledge of cloud AI offerings and environment configuration.

* Messaging & Async Processing:

Experience with event-driven workflows (Rabbit

MQ, Kafka, SQS) is a plus.

* Monitoring & Logging:
Implementing logging (Python logging, Cloud Watch, Application Insights) for tracking and debugging.

* Frameworks for UI (optional):
Familiarity with Streamlit or basic web development for internal agent demos.

* Security & Compliance Tools:
Handling OAuth, encryption, and compliance libraries for regulated industries.

* Source Data Tools:
Using NLP libraries for text preprocessing and…