Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You'll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your Role And Responsibilities

As a Software Developer you'll participate in many aspects of the software development lifecycle, such as design, code implementation, testing, and support. You will create software that enables your clients hybrid-cloud and AI journeys.

You'll have the opportunity to work with the latest technologies, ensuring the applications delivered are high performing, highly available, responsive, and maintainable.

Your Primary Responsibilities Include

Analytical Problem-Solving and Solution Enhancement: Analyze, validate and propose improvements to existing failures, with the support of the architect and technical leader.
Comprehensive Engagement Across Process Phases: Involvement in every step of the process, from design, development, testing release changes and troubleshoot where necessary, providing a great customer service.
Strategic Stakeholder Engagement and Innovative Coding Solutions: Drive key discussions with your stakeholders and analyze the current landscape for opportunities to operate and code creative solutions.

Required Technical And Professional Expertise

Role1: Cloud Full Stack Engineer

Expertise in conceptualization and design of new and efficient Agents to perform common and repeatable integration between applications and systems leveraging GenAI and Agentic AI capabilities to produce architectural artifacts
Ability to generate API definition for integrating different applications and use frameworks (e.g. Swagger Codegen, etc.) to generate code structure using GenAI, Agentic AI and Assistants
Expertise of various routing protocols and stateful streaming (e.g. Apache Flink, Kafka, etc.) for workflow orchestration and event driven operations
Deep knowledge across hyperscalars (e.g. AWS, Azure, Red Hat, etc.) and deployment models for an optimized resource usage and cost
Expertise in designing continuous integration, continuous delivery and automation workflows (e.g. GITOPs pipeline using Argo CD, Jenkins, Terraform, etc.)
Ability to define data mapping and data lineage leveraging the GenAI and Agentic AI tools and assistants
Ability to define data pipelines across zones (i.e. Bronze to Silver to Gold) as per the Medallion architecture using GenAI models and tools

Role2: Experience Frontend Engineer

Leverages GenAI, AI Agents and Assistants in Web and Frontend engineering activities to deliver responsive web applications using languages (e.g., JavaScript, TypeScript) and frameworks (e.g., React, Angular, Vue.js)
Build new and efficient Agents to perform common and repeatable activities such as component generation, accessibility audits, and performance tuning
Ability to engineer and deploy web and frontend applications and products using state-of-the-art design patterns, integrating UI/UX components, and handling client-side data management
Expertise in multiple layers of web architectures, including languages (e.g., JavaScript, TypeScript), frameworks (e.g., React, Angular), libraries (e.g., Redux, Tailwind CSS), and frontend state management tools
Apt in applying continuous integration, continuous delivery methodologies and GenAI tools to ensure efficient engineering, testing (e.g., cross-browser compatibility), and deployment of web applications to hosting environments and CDNs

Role3: AI Engineer/ AI Developer

What You'll Do

Design and deploy agentic AI (multi-agent, planner-executor, RAG-enabled) aligned to client workflows and compliance.
Prototype, fine-tune, and validate LLM/multimodal agents for end-to-end automation and decision support.
Build orchestration, state management, connectors, and tool-integration layers for reliable agent actions.
Develop next‑generation agentic AI systems and intelligent virtual agents
Productionize agents with latency/cost optimization, autoscaling, observability, CI/CD on hybrid-cloud (OpenShift/Azure/AWS).
Implement safety, alignment, explainability, audit trails, intent validation, and failure-mode handling.
Lead client scoping, co-creation workshops, technical demos, and iterative customization to meet SLAs.
Continuously monitor, test, and refine reward/feedback loops to improve agent outcomes and ROI.
Build and deploy enterprise‑scale AI/ML models
Analyze real‑world datasets to deliver high‑value insights
Innovate in agile squads with IBM engineers, architects, and researchers

What We're Looking For

Strong foundations in AI, ML, deep learning, NLP, and algorithms
Hands‑on experience with Python and modern AI/ML frameworks
Curiosity and passion for agentic AI, autonomous workflows, and next‑gen automation
Strong analytical thinking, problem‑solving, and communication skills
Drive to apply AI to solve meaningful, real‑world problems

Role4: Associate Data Engineer

Required Professional And Technical Expertise

Bachelor's degree in computer science / information technology / data science / engineering.
Strong fundamentals in programming and problem solving, with hands-on knowledge of Python and SQL.
Understanding of data structures, algorithms, debugging and analytical thinking to solve real-world data problems.
Strong knowledge in RDBMS concepts including tables, keys, indexing, normalization, joins, transactions and query optimization basics.
Working knowledge of SQL for data extraction, transformation and analysis.
Understanding of big data / distributed processing fundamentals (Spark concepts).
Exposure to Python libraries such as Pandas / Polars for data processing and analysis.
Knowledge on using Git / GitHub / GitLab for source control and collaborative development.
Exposure to building REST services / microservices using FastAPI/Flask.
Basic understanding of GenAI fundamentals such as LLMs, prompting, embeddings, and real-world usage patterns.
Awareness of AI agent concepts using frameworks like LangChain including RAG, tool calling, and orchestration patterns
Strong communication skills with ability to collaborate with technical and non-technical stakeholders.
Willingness and ability to learn cloud native services, Databricks and Snowflake

Preferred Professional And Technical Expertise

Internship / academic project experience in data engineering, analytics, ETL pipelines or backend engineering.
Exposure to Spark / PySpark through projects, coursework or certifications.
Familiarity with unit testing basics and development best practices.
Exposure to GenAI tools (example: ChatGPT, GitHub Copilot or open-source LLM tools) for improving engineering productivity.
Knowledge of basic data warehousing concepts and batch vs streaming processing.
Strong interest in building a career in Data Engineering / Data Platforms / Lakehouse architectures.
Good to have Python PCEP certification

Preferred Technical And Professional Experience

Engineering Background, Problem Solving.
Good interpersonal skills.
Should be flexible to work from anywhere in India.
Year of Pass out 2026.
BE / BTech Computer Science (all CS batches like CSE, AIML , DS , Cloud Computing, Big Data Analytics, CSBS , IOT , Robotics, AI , Cyber Security, Block Chain to name a few) or Information Technology with 6 CGPA/60%.
Fluent Communication skills (written and spoken).
No active backlogs.