Software Engineer
Posted
Summary
Infor is hiring a Software Engineer in Hyderabad to build and maintain production-grade Python services, write and tune PySpark jobs for large-scale data processing, and integrate LLM/ML capabilities such as RAG and inference pipelines into dependable systems. Core focus is Python craftsmanship, SQL/database work, testing, and operationalizing AI workloads.
The Software Engineer (Python / PySpark / AI) is responsible for designing, developing, and maintaining production-grade Python applications and services, with a strong emphasis on core software engineering fundamentals, clean design, and solid database work.
The role includes writing and tuning PySpark jobs for large-scale data
processing, and building AI-enabled capabilities such as LLM-backed services, retrieval pipelines, and ML feature/inference workflows. Depth in Python, databases, and engineering craftsmanship remains the primary focus. AI work is treated as software engineering: the engineer is expected to integrate models into dependable systems with proper evaluation, observability, and cost controls, rather than to conduct original model research. Exposure to broader data engineering practices such as orchestration, lakehouse formats, and pipeline design is valuable.
The engineer collaborates closely with other developers, architects, data scientists, and project managers to translate business requirements into working software.
The role includes writing and tuning PySpark jobs for large-scale data
processing, and building AI-enabled capabilities such as LLM-backed services, retrieval pipelines, and ML feature/inference workflows. Depth in Python, databases, and engineering craftsmanship remains the primary focus. AI work is treated as software engineering: the engineer is expected to integrate models into dependable systems with proper evaluation, observability, and cost controls, rather than to conduct original model research. Exposure to broader data engineering practices such as orchestration, lakehouse formats, and pipeline design is valuable.
The engineer collaborates closely with other developers, architects, data scientists, and project managers to translate business requirements into working software.
- Design, develop, and maintain scalable, high-quality applications and services in Python.
- Write clean, maintainable, well-tested, and efficient code following established design principles and team standards.
- Develop and optimize PySpark jobs for batch and large-volume data processing workloads.
- Build AI-enabled features: integrate LLM and ML APIs or self-hosted models into backend services, including prompt design, structured output handling, tool/function calling, and graceful fallback behavior.
- Implement retrieval-augmented generation (RAG) components: chunking and embedding pipelines, vector search, retrieval quality tuning, and grounding/citation handling.
- Build data preparation and feature pipelines that feed model training, fine-tuning, and inference, using Spark where scale requires it.
- Operationalize AI workloads: batch and real-time inference paths, caching, token and cost monitoring, latency budgets, rate limiting, and retry semantics.
- Contribute to AI evaluation and quality: build regression test sets, offline evaluation harnesses, and guardrails for accuracy, hallucination, safety, and PII handling.
- Model, query, and tune relational data: write efficient SQL, design schemas, and reason about indexes, joins, and query plans.
- Build and consume REST APIs and integrations with backend, data, and AI services.
- Troubleshoot and debug production issues, including performance bottlenecks, memory issues, data correctness problems, and non-deterministic model behavior.
- Profile and optimize applications, Spark jobs, and AI inference paths for runtime, resource usage, and cost.
- Use AI-assisted development tools responsibly to improve delivery speed while maintaining code quality, security, and review standards.
- Participate in code reviews, design discussions, and technical documentation.
- Work independently and as part of a team to deliver projects on time.
- Bachelor's degree in Computer Science or a related field.
- 3-5 years of experience building applications in Python.
- Strong command of core Python: data structures, OOP, error handling, iterators/generators, concurrency basics, packaging, and virtual environments.
- Solid computer science fundamentals: data structures, algorithms, complexity analysis, and debugging methodology.
- Strong SQL skills and hands-on experience with at least one relational database (PostgreSQL, MySQL, Oracle, or SQL Server).
- Working knowledge of PySpark or a strong willingness to develop it on the job, including DataFrame APIs and basic understanding of distributed execution.
- Practical exposure to AI/ML in software: consuming LLM or ML model APIs, working with embeddings, or integrating a model into an application, with an understanding of prompt behavior, context limits, and non-determinism.
- Experience with automated testing (pytest or equivalent) and writing testable code.
- Experience with Git or other version control systems.
- Excellent problem-solving and analytical skills.
- Ability to work independently and as part of a team.
- Strong communication skills.
- Hands-on experience with LLM application frameworks and tooling (LangChain, LlamaIndex, Semantic Kernel, or equivalent) and agentic/tool-calling patterns.
- Experience with vector databases or vector search (pgvector, OpenSearch, FAISS, Pinecone, Milvus, or similar).
- Experience with the Python ML/data stack (pandas, NumPy, scikit-learn) and familiarity with PyTorch or TensorFlow.
- Exposure to MLOps and LLMOps practices: model/prompt versioning, experiment tracking (MLflow or similar), model registries, deployment, drift monitoring, and AI observability/tracing.
- Familiarity with fine-tuning or parameter-efficient tuning approaches, and knowing when retrieval or prompting is the better trade-off.
- Awareness of responsible AI concerns: data privacy, PII redaction, bias, prompt injection, and secure handling of model inputs and outputs.
- Experience tuning Spark jobs: partitioning, shuffles, joins, caching, and reading execution plans.
- Familiarity with data engineering practices such as ETL/ELT pipeline design, incremental loads, and data quality checks.
- Exposure to lakehouse table formats (Delta Lake, Iceberg, Hudi) or columnar formats (Parquet, ORC).
- Experience with workflow orchestration tools (Airflow, Dagster, or similar).
- Working knowledge of cloud platforms (AWS, Azure, or GCP), managed Spark offerings (EMR, Databricks, Glue), and managed AI services (Amazon Bedrock/SageMaker, Azure OpenAI, or Vertex AI).
- Experience with containers and CI/CD (Docker, Kubernetes, GitLab CI, or Jenkins).
- Familiarity with NoSQL or streaming systems (Kafka, DynamoDB, MongoDB).
- Familiarity with Agile development methodologies is a plus.
Skills
- Agentic AI
- Agile
- AI
- Airflow
- API
- AWS
- AWS Bedrock
- Azure
- CI/CD
- Cloud
- Dagster
- Data Engineering
- Data Quality
- Databricks
- Delta Lake
- Docker
- DynamoDB
- ELT
- Embeddings
- ETL
- FAISS
- Fine Tuning
- GCP
- Git
- GitLab
- Jenkins
- Kafka
- Kubernetes
- Lakehouse
- LangChain
- LlamaIndex
- LLM
- LLMOps
- Machine Learning
- Milvus
- MLflow
- MLOps
- MongoDB
- MySQL
- NoSQL
- NumPy
- Observability
- OOP
- OpenAI
- OpenSearch
- Oracle
- pandas
- Parquet
- pgvector
- Pinecone
- PostgreSQL
- PySpark
- pytest
- Python
- PyTorch
- RAG
- REST
- SageMaker
- scikit-learn
- Semantic Kernel
- Spark
- SQL
- SQL Server
- TensorFlow
- Test Automation
- Vector Databases
- Vector Search
- Version Control
- Vertex AI
- Workflow Orchestration