Full Stack Developer (Java)

1. Essential Duties and Responsibilities

• Develop and support scalable, extensible, and highly available data pipelines on heterogeneous datasets that power downstream applications and systems and serve content to our web and API products.
• Closely collaborate with partners across product and design, engineering, and business teams to drive innovations that improve our customers’ experience.
• Follow software-development best practices including test-driven development, contributing to documentation, feature flagging, etc.
• Help to maintain and improve existing ETL pipelines.
• Work with your team to troubleshoot and fix issues in ingest and processing, considering dependencies and integration points.
• Collaborate with DevOps to plan resources and continuously optimize the infrastructure and configuration of our data pipelines to ensure a healthy and high-performance production deployments.

2. Skills And Experience

• Strong expertise in advanced data modeling, schema and ETL process design, implementation, and maintenance.
• Experience with numerous data lake / warehouse technologies (Databricks, Snowflake, Presto, Dremio).
• Strong knowledge of data lake best practices.
• Strong background in advanced SQL and focused on understanding, manipulating, processing and extracting values from large datasets and data streams.
• Cloud experience (Azure or AWS preferred).
• Advanced Python skills or Java skills.
• Experience with different variety of data types (JSON, Parquet, Excel, Flat files).
• Experience with databases and data storage frameworks including: Microsoft SQL, Postgresql, Elasticsearch, Mongo, Cosmos DB, Delta Lake.
• Expertise in cloud messaging platforms including Apache Kafka or Azure Service Bus.
• Solid comprehension of common design patterns, algorithms, and data structures.
• Working knowledge of containerization and modern cloud deployments including Docker and Kubernetes.
• Bachelor’s degree in computer science, or a related field.
• Excellent communication and presentation skills.

Python Developer

1. Role Description

• Build and deploy production-grade AI/ML pipelines using Python — from data ingestion to model serving via REST APIs.
• Develop LLM-powered applications and GenAI features: RAG pipelines, prompt engineering, tool-use agents and fine-tuning workflows.
• Build scalable backend APIs using FastAPI or Django REST Framework, and deploy on AWS (EC2, Lambda, ECS).
• Integrate with vector databases (Pinecone, Weaviate, ChromaDB) and LLM providers (OpenAI, Anthropic, Hugging Face).
• Collaborate closely with the AI/ML team to bring models from research into reliable, maintainable production systems.

2. Required Skills

• Python (proficient) — clean, well-tested, production-ready code
• LLM integration — OpenAI API, Anthropic Claude, LangChain, LlamaIndex
• GenAI — RAG, prompt engineering, embeddings, vector search
• Frameworks — FastAPI, Django, Django REST Framework
• Cloud — AWS (EC2, Lambda, S3, RDS) or GCP
• Databases — PostgreSQL, MongoDB, Redis
• Tools — Docker, Git, CI/CD pipelines

3. Nice to Have

• Experience with Agentic AI frameworks (AutoGen, CrewAI, LangGraph)
• ML/Data Science background (PyTorch, TensorFlow, Scikit-learn)
• Front-end basics (React, TypeScript) for full-stack capability
• Familiarity with fine-tuning LLMs (LoRA, QLoRA, RLHF)

Data Engineer

1. Essential Duties and Responsibilities

• Develop, deploy, and support real-time, automated, scalable data streams from a variety of sources into the data lake or data warehouse.
• Develop and implement data auditing strategies and processes to ensure data quality; identify and resolve problems associated with large scale data processing workflows; implement technical solutions to maintain data pipeline processes and troubleshoot failures.
• Collaborate with technology teams and partners to specify data requirements and provide access to data.
• Tune application and query performance using profiling tools and SQL or other relevant query language.
• Understand business, operations, and analytics requirements for data.
• Build data expertise and own data quality for assigned areas of ownership.
• Work with data infrastructure to triage issues and drive to resolution.

2. Required Qualifications

• Bachelor’s Degree in Data Science, Data Analytics, Information Management, Computer Science, Information Technology, related field, or equivalent professional experience.
• Overall experience should be more than 7+ years.
• 3+ years of experience working with SQL.
• 3+ years of experience in implementing modern data architecture-based data warehouses.
• 2+ years of experience working with data warehouses such as Redshift, BigQuery, or Snowflake and understand data architecture design.
• Excellent software engineering and scripting knowledge.
• Strong communication skills (both in presentation and comprehension) along with the aptitude for thought leadership in data management and analytics.
• Expertise with data systems working with massive data sets from various data sources.
• Ability to lead a team of Data Engineers.

3. Preferred Qualifications

• Experience working with time series databases.
• Advanced knowledge of SQL, including the ability to write stored procedures, triggers, analytic/windowing functions, and tuning.
• Advanced knowledge of Snowflake, including the ability to write and orchestrate streams and tasks.
• Background in Big Data, non-relational databases, Machine Learning and Data Mining.
• Experience with cloud-based technologies including SNS, SQS, SES, S3, Lambda, and Glue.
• Experience with modern data platforms like Redshift, Cassandra, DynamoDB, Apache Airflow, Spark, or ElasticSearch.
• Expertise in Data Quality and Data Governance.