SB-1439-AI Engineer

Softobiz Technologies India

Job Location:

Kochi - India

Monthly Salary: Not Disclosed

Posted on: 9 days ago

Vacancies: 1 Vacancy

Department:

Software Development

Job Summary

AI Engineer

Role Summary

We are looking for AI Agent Architects to design and build production-grade agentic AI systems. This is a deeply hands-on engineering role centred on multi-agent orchestration advanced context management and large language model (LLM) integration strong data structures and algorithms (DSA) skills and hands-on ability with Python.

You will work on the design of agent workflows and the context architecture that makes them reliable accurate and efficient taking systems from prototype to production. The ideal candidate has a strong academic record sharp problem-solving ability and genuine enthusiasm for going deep on the agentic AI stack.

Key Responsibilities

Agent Orchestration & Workflow

Design and implement multi-agent workflows using LangGraph on Python with Pydantic structured output.
Model complex long-running processes as stateful resumable graphs with branching looping retries and durable checkpointing.
Implement safe pause/resume and human-in-the-loop (HITL) checkpoints.

Context Engineering

Engineer context management as a first-class subsystem layered context retrieval/indexing and active working sets.
Implement deterministic context selectors and filters token-budgeted prompts and summarisation/compaction of long histories.
Design typed context schemas so each agent step receives precise high-signal context.

LLM Integration & Retrieval

Integrate LLM providers (e.g. Anthropic OpenAI / Azure OpenAI and self-hosted models) using robust prompt engineering tool calling and structured output.
Wire in retrieval vector search and embeddings and code-intelligence techniques for working over large codebases.
Contribute to model-routing logic that balances task type risk latency and cost.

Quality Evaluation & Governance

Build evaluation and error-analysis loops; treat failures as feedback that improves reliability over time.
Implement verification and validation patterns and deterministic gates for agent outputs.
Ensure agent decisions and context are observable auditable and reproducible.

Collaboration

Partner with platform/infrastructure engineers on deployment inference persistence and durable execution.
Contribute to engineering standards design reviews and code quality.

Required Technical Skills

Domain	Skills & Technologies	Must / Preferred
CS Fundamentals & DSA	Data structures algorithms complexity analysis strong problem-solving	Must
Programming	Python 3.10 (async typing); clean idiomatic code	Must
Agent Orchestration	LangGraph graphs/state machines checkpointers HITL interrupts	Must
Context Engineering	Layered context selectors/filters summarisation & compaction token budgeting	Must
Agentic AI Development	Multi-agent design tool calling structured output verification patterns	Must
LLM Integration	Anthropic & OpenAI / Azure OpenAI SDKs prompt engineering	Preferred
Data Modelling	Pydantic v2 JSON Schema / typed contracts	Preferred
Retrieval	Vector stores (e.g. Qdrant / Azure AI Search) embeddings	Preferred
Context Protocol	Model Context Protocol (MCP) resources/tools Streamable HTTP	Preferred
Multi-agent Frameworks	CrewAI Microsoft Agent Framework	Preferred
Durable Workflows	Temporal (long-running resumable flows)	Preferred
Inference	vLLM awareness (paged attention batching quantisation) model routing	Preferred

Qualifications & Certifications

Strong academic record / B.E. / / MCA in Computer Science or a related field from a reputable institution (or equivalent).
Strong data structures algorithms and problem-solving skills a competitive-programming track record (Codeforces / LeetCode / ICPC / similar) is a strong plus.
Hands-on Python plus exposure to LLM / agentic AI through academic projects internships or work with clear eagerness to go deep on LangGraph and context engineering.

Preferred Certifications

Microsoft Certified: Azure AI Engineer Associate
Any recognised cloud certification (Azure / AWS / GCP) is a plus

Soft Skills & Cultural Fit

Strong analytical mindset with a structured approach to design debugging and root-cause analysis.
Clear written and verbal communication able to explain agent and context design to technical and non-technical stakeholders.
Comfortable with ambiguity and able to work independently with minimal supervision.
Collaborative team player who contributes to shared standards code reviews and knowledge sharing.

What We Offer

Deep hands-on work with the modern agentic AI stack LangGraph MCP and multi-agent systems.
High ownership and influence over architecture from an early stage.
Competitive compensation with a structured performance review process.
Professional development support certifications conferences and access to emerging tooling.
Collaborative transparent culture with clear growth pathways toward Staff / Principal engineering.

Required Experience:

AI Engineer Role Summary We are looking for AI Agent Architects to design and build production-grade agentic AI systems. This is a deeply hands-on engineering role centred on multi-agent orchestration advanced context management and large language model (LLM) integration strong data structures and a...