Hands-on AI Engineers to set up and operationalize an AI platform that supports agentic workflows for the Software Development Life Cycle (SDLC) and Operations……
In this role, you will define and lead the architecture for Large Language Model (LLM) powered and Agentic AI systems embedded into our enterprise SaaS platform……
You have working views on which model providers to use for what, which orchestration patterns hold up under real load, where the abstractions in popular agent……
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You'll design, build, and operate agentic systems and AI-powered automations that touch real production workflows - both internal (engineering, QA, support……
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected……
Act as a technical product partner: collect user requirements, define success metrics and acceptance criteria, translate needs into solution designs and……
Demonstrate unwavering dedication to delivering impactful AI solutions that consistently surpass client expectations and align with strategic goals.…
With hands-on industry experience, you are expected to bring expertise in AI system design, ML engineering, LLM deployment, and scalable software development……
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As a Lead/Principal Engineer in the Epsilon Attribution/Forecasting Product Development team, you will design, implement, and optimize data processing solutions……
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We’re committed to fostering an inclusive environment where everyone can thrive. Your focus is on writing clean, efficient code to integrate LLMs into our……
This is a space where you can challenge yourself, set new standards and perform beyond expectations for yourself, our clients, and our industry.…
AI Systems Engineer responsible for building and scaling AI-powered backend systems, agent workflows, data infrastructure, and automation platforms that support……
Collaborative mindset with excellent communication skills to work effectively with cross-functional teams in an agile environment, including product managers,……
For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs.…
Design and implement context architectures for LLM apps and agents: schemas, memory patterns, context assembly, and context window management.
Build and optimize RAG pipelines (chunking, embeddings, hybrid retrieval, re-ranking) and validate quality using repeatable evaluation harnesses.
Develop system prompts, prompt libraries, and structured output patterns; harden solutions against prompt injection and jailbreak attempts.
Implement agentic workflows and tool-use integrations (APIs, function calling, workflow engines) with clear guardrails and observability.
Engineer memory and persistence patterns (session memory, episodic recall, vector memory) appropriate for enterprise privacy and retention needs.
Work with enterprise data and app teams to connect AI solutions to real systems (ERP/SCM/CRM, data lakes/warehouses), ensuring secure access and correct semantics.
Collaborate with delivery leads to break work into stories, estimate effort, and drive day-to-day execution; mentor engineers through reviews and pairing.
Use systematic optimization (prompt/context tuning, retrieval experiments, DSPy-style approaches) to improve reliability, latency, and cost.
What You Bring
AI Engineering Skills
Strong Python skills and ability to ship production services.
Hands-on expertise with RAG: embeddings, vector stores, retrieval strategies, re-ranking, and grounding techniques.
Strong prompt and context engineering: system prompts, structured outputs, tool-use prompting, and context assembly patterns.
Experience building agentic systems with orchestration frameworks (or custom implementations) and designing safe tool integrations.
Awareness of security threats (prompt injection, data exfiltration) and ability to implement practical mitigations and guardrails.
Enterprise Integration Background
Experience integrating AI apps with enterprise services, data sources, and identity (SSO/IAM), including secure network and secrets handling.
Ability to work with structured and unstructured enterprise data; understand governance/lineage enough to avoid incorrect or unsafe data use.
Comfort operating within enterprise SDLC controls: CI/CD, change management, security reviews, and production incident response.
Working knowledge of enterprise workflows and process context so AI solutions map to real operations and decision points.
Tools & Platforms
Python; common LLM/RAG frameworks (LangChain, LlamaIndex, Haystack or equivalent).
Vector databases and search stacks (pgvector, Pinecone, Weaviate, Milvus, Elasticsearch/OpenSearch).
Memory and state management approaches (session stores, vector memory, durable stores) appropriate for privacy and retention constraints.
Evaluation and observability tooling (RAGAS, LangSmith/Phoenix or equivalent) and ability to build custom eval pipelines.
Preferred Qualifications
B.Tech / M.Tech in CS, Engineering, or Linguistics; research background in NLP or information retrieval is a plus.
Published work, open-source contributions, or internal frameworks related to context management or prompt engineering.
Prior consulting or professional services experience — ability to adapt context design to diverse client environments quickly
The minimum salary is ₹5L and the max salary is ₹8L.
₹5L – ₹8L/yr (Glassdoor Est.)
₹6L
/yr Median
Chennai
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