SB-1486-AI Engineer Intern

Softobiz; Ernakulam; On-site; INR 201,000 - 279,000; Full time

Job summary

Softobiz is seeking AI Engineer Interns to learn and contribute to production‑grade agentic AI systems. The role focuses on multi‑agent orchestration, context management, and LLM integration, with mentorship and hands‑on Python work.

Open to final‑year students and recent graduates; strong CS fundamentals and DSA skills are prioritized. This internship offers hands‑on experience under senior engineers and exposure to cutting‑edge AI tooling and workflows.

Qualifications

• Pursuing or recently completed CS degree in Computer Science or related field.
• Final-year students and recent graduates welcome; fundamentals matter most.
• Strong data structures and algorithms skills; competitive programming is a plus.
• Hands-on Python experience with interest in LLM/agentic AI.

Responsibilities

• Design and implement multi-agent workflows with LangGraph in Python.
• Model processes as stateful, resumable graphs with checkpointing.
• Implement safe pause/resume and HITL checkpoints.
• Develop layered context management and context selectors.
• Wire in LLM providers and retrieval with embeddings.
• Experiment with model routing balancing task type, latency, and cost.
• Build evaluation loops and deterministic gates.
• Participate in design reviews and code reviews.

Skills

CS Fundamentals

DSA

Python

LangGraph

Context Engineering

LLM Integration

Retrieval

Vector Stores

HITL

Multi-agent AI

Education

B.Tech/B.E./M.Tech/MCA

Tools

Pydantic

JSON Schema

LangGraph

Anthropic SDK

OpenAI SDK

Job description

Role Summary

We are looking for five AI Engineering Interns to learn and contribute to production-grade agentic AI systems alongside our engineers. This is a hands‑on, mentored internship centred on multi‑agent orchestration, context management, and large language model (LLM) integration. It is open to final‑year students and recent graduates — what matters most is outstanding computer‑science fundamentals, strong data structures and algorithms (DSA) skills, and hands‑on ability with Python.

AI Engineer Intern

Role Summary

We are looking for five AI Engineering Interns to learn and contribute to production‑grade agentic AI systems alongside our engineers. This is a hands‑on, mentored internship centred on multi‑agent orchestration, context management, and large language model (LLM) integration. It is open to final‑year students and recent graduates — what matters most is outstanding computer‑science fundamentals, strong data structures and algorithms (DSA) skills, and hands‑on ability with Python.

Key Responsibilities

Agent Orchestration & Workflow

• Assist in designing and implementing multi-agent workflows using LangGraph on Python with Pydantic structured output, under the guidance of senior engineers.
• Help model processes as stateful, resumable graphs with branching, looping, retries, and checkpointing.
• Support implementation of safe pause/resume and human‑in‑the‑loop (HITL) checkpoints.

Context Engineering

• Learn and contribute to context management — layered context, retrieval/indexing, and active working sets.
• Help implement context selectors and filters, token‑budgeted prompts, and summarisation/compaction of long histories.
• Assist in designing typed context schemas so each agent step receives precise, high‑signal context.

LLM Integration & Retrieval

• Integrate LLM providers (e.g. Anthropic, OpenAI / Azure OpenAI) using prompt engineering, tool calling, and structured output, with mentorship.
• Help wire in retrieval — vector search and embeddings — and code‑intelligence techniques for working over large codebases.
• Contribute to model‑routing experiments that balance task type, latency, and cost.

Quality, Evaluation & Governance

• Help build evaluation and error‑analysis loops; learn to treat failures as feedback that improves reliability.
• Assist in implementing verification and validation patterns and deterministic gates for agent outputs.
• Help keep agent decisions and context observable, auditable, and reproducible.

Collaboration

• Work with platform/infrastructure engineers on deployment, inference, and persistence tasks.
• Participate in design reviews, code reviews, and Demo Friday — sharing your work, including failed experiments.

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 Good to have Context Engineering Layered context, selectors/filters, summarisation & compaction, token budgeting Good to have Agentic AI Development Multi-agent design, tool calling, structured output, verification patterns Good to have 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

• Pursuing or recently completed B.Tech / B.E. / M.Tech / MCA in Computer Science or a related field from a reputable institution (or equivalent).
• Final‑year students and recent graduates welcome; strong fundamentals matter more than years of experience.
• 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 any exposure to LLM / agentic AI through academic projects or self‑learning — with clear eagerness to go deep on LangGraph and context engineering.

Preferred Certifications

• Any recognised AI/ML or agentic-AI online course or certification (e.g. DeepLearning.AI, Anthropic, Microsoft Azure AI Fundamentals).
• Any cloud fundamentals 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 your approach to technical and non‑technical people.
• Eagerness to learn, high coachability, and the ability to take and act on feedback.
• Collaborative team player who contributes to shared standards, code reviews, and knowledge sharing.