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Software Engineer ( Python + PySpark + Agentic AI ) - 5 + Years

Infor

  • Hyderabad, India
  • Competitive Package
  • Fresher (0-1 Yrs)
  • Posted Today
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About the role

Department: Development

Location: Hyderabad

Description

We are looking for an experienced Python + PySpark Developer with a strong background in designing, developing, and optimizing large-scale data processing solutions. The ideal candidate should have strong expertise in Python, PySpark, SQL, and big data technologies. You will build the large-scale data pipelines and agentic AI features that power our platform on AWS.

You will apply strong Python and PySpark skills to data processing on EMR on EKS, and build LLM-based agents that assist and automate work on our data under human oversight.

A Typical Day In The Life Includes

  • Write and tune PySpark jobs on EMR (EMR on EKS) and Delta Lake to prepare and transform data at scale
  • Design and build LLM-powered agents that plan tasks, call tools, and propose or apply actions against our services and data, under confidence thresholds and human-in-the-loop review
  • Define the tools and APIs agents can use, and connect agents to internal systems (e.g., via Model Context Protocol)
  • Build retrieval-augmented (RAG) pipelines: chunking, embeddings, vector search, and grounding responses in our data
  • Design agent workflows with clear guardrails, confidence gating, human-in-the-loop exception handling, and fallback behavior
  • Write prompts and evaluation harnesses; run evaluation in CI and measure agent quality, cost, and latency, then iterate
  • Build and maintain Python services and shared libraries, and Kafka producers/consumers for moving data between services
  • Serve agents behind APIs and monitor them with LLM/agent observability (quality, cost, latency, failures)
  • Write design docs and collaborate with engineers and data scientists on architecture decisions
  • Use AI coding agents in your own day-to-day workflow while owning correctness and quality

Basic Qualifications

  • These are the core skills we screen for. A strong candidate has all three.
  • Python 3 — solid experience writing production code, with strong fundamentals and readable, tested code, using a web framework such as FastAPI, Flask, or Django
  • PySpark & SQL — hands-on experience writing and tuning Spark jobs on EMR (we run EMR on EKS), reading/writing Delta Lake tables, and strong SQL for large-scale data
  • Agentic AI — hands-on experience building LLM or agent-based features: tool/function calling, multi-step agent workflows, RAG, and grounding output in real data.
  • Experience writing production Python 3, including designing and optimizing large-scale data processing solutions
  • Experience building services or APIs with a Python web framework such as FastAPI, Flask, or Django
  • Hands-on experience with Spark / PySpark, ideally on EMR (EMR on EKS a plus)
  • Strong SQL and familiarity with big data technologies
  • Hands-on experience building LLM or agent-based features (tool calling, function calling, or multi-step agent workflows)
  • Experience with an agent or LLM framework or platform such as Amazon Bedrock, LangChain, LangGraph, LlamaIndex, or the OpenAI/Anthropic SDKs
  • Experience with RAG: embeddings, vector search, and grounding model output in real data
  • Practical prompt engineering and experience evaluating LLM/agent output for quality and reliability
  • Experience handling sensitive data and PII responsibly, including data-governance and privacy considerations when sending data to external LLMs
  • Experience with at least one database (MongoDB/DocumentDB, PostgreSQL, or similar)
  • Experience with AWS services such as S3 and EMR
  • Experience writing tests with pytest (or similar) and working with Git
  • Comfortable with Docker and Linux
  • Practical experience using AI coding assistants or agents (e.g., Kiro, Cursor, GitHub Copilot, Claude Code) in real projects
  • Strong communication: able to write design docs and collaborate across teams

Preferred Qualifications

  • Experience with Model Context Protocol (MCP) or wiring agents to external tools and data sources
  • Experience keeping LLM systems reliable: guardrails, evaluation pipelines, cost/latency tuning, caching
  • LLM/agent observability and lifecycle experience: prompt/version management, monitoring, and quality regression detection
  • Solid ML fundamentals and quality metrics (precision, recall, F1); familiarity with scikit-learn, pandas, NumPy
  • Delta Lake, AWS Glue, and Athena
  • Kafka streaming pipelines
  • Kubernetes / EKS, Helm, and GitOps deployment
  • Experience in a polyglot microservice environment (Node.js / Python / Java)
  • Awareness of bias and explainability in AI systems

About Infor

Infor is where ambition meets impact. Join a global community of bold thinkers and innovators, where your expertise doesn't just solve problems. it shapes industries, unlocks opportunities, and creates real-world impact for billions of people. At Infor, you're not just building a career. you're helping to build what's next.

Infor is a global leader in business cloud software products for companies in industry specific markets. Infor builds complete industry suites in the cloud and efficiently deploys technology that puts the user experience first, leverages data science, and integrates easily into existing systems. Over 60,000 organizations worldwide rely on Infor to help overcome market disruptions and achieve business-wide digital transformation.

For more information visit www.infor.com

Our Values

At Infor, we strive for an environment that is founded on a business philosophy called Principle Based Management™ (PBM™) and eight Guiding Principles: integrity, stewardship & compliance, transformation, principled entrepreneurship, knowledge, humility, respect, self-actualization.

We have a relentless commitment to a culture based on PBM™. Informed by the principles that allow a free and open society to flourish, PBM™ prepares individuals to innovate, improve, and transform while fostering a healthy, growing organization that creates long-term value for its clients and supporters and fulfillment for its employees.

Infor is an Equal Opportunity Employer. We are committed to creating a diverse and inclusive work environment. Infor does not discriminate against candidates or employees because of their sex, race, gender identity, age, sexual orientation, religion, national origin, veteran status, or any other protected status under the law.

If you require accommodation or assistance at any time during the application or selection processes, please submit a request by following the directions located in the FAQ section.

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Eligibility & process

Who can apply

Employment type: Full-time

Skills & stack

Python Java JavaScript SQL Next.js Node.js Django FastAPI

Selection process

Details are shared by the recruiter after shortlisting.

Prep kit

20 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Python, Java, JavaScript, SQL, Next.js, Node.js, Django, FastAPI) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Infor Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 5
What is the difference between WHERE and HAVING clauses in SQL? Answer
Model answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer
Model answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer
Model answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer
Model answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer
Model answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Behavioural & HR questions 3
Why do you want to join Infor as a Software Engineer ( Python + PySpark + Agentic AI ) - 5 + Years?
Highlight Infor's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Infor Competitive Package
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