Top Tech Jobs & Startup Jobs

Build predictive models, end-to-end data science pipelines, and cloud-deployed AI/ML APIs. Develop image classification, NLP, chatbot, RAG, and LLM-agent solutions while monitoring data quality, drift, governance, performance, latency, and scalability. Collaborate with cross-functional teams to deliver production machine-learning projects and translate business requirements into technical solutions.

Develop and maintain software products using Python. Responsibilities include writing scalable code, building backend components, integrating user-facing features and third-party services, testing and debugging applications, improving existing systems, implementing security and data protection, prioritizing features, and collaborating with internal teams to deliver technical solutions.

Develop modular Python systems for AI-driven code modernization using LangChain, LangGraph, LLMs, workflow orchestration, and state management. Build RAG pipelines, vector searches, dependency matrices, database persistence, and code analysis workflows. Integrate multiple LLM providers, create robust tooling, and maintain tests, logging, and error handling. Collaborate with client engineering teams, participate in SAFe Agile ceremonies, and document work in JIRA.

Build and implement functional software programs throughout the development lifecycle. Responsibilities include designing algorithms, writing clean code, integrating components, testing, deploying, debugging, upgrading systems, evaluating user feedback, recommending improvements, and creating technical documentation. The role requires Agile familiarity, test-driven development knowledge, programming experience, database and ORM knowledge, strong troubleshooting, communication, adaptability, and attention to detail.

Build production-grade data ingestion, processing, warehousing, and migration solutions at scale. Design pipelines and data systems using cloud platforms, Snowflake, Redshift, and enterprise databases. Transform and integrate data from multiple sources, write advanced SQL and data transformations, and support large-scale migrations from on-premises systems to cloud warehouses. Collaborate with stakeholders to solve ambiguous problems and align data systems with business goals.

Execute manual test cases, analyze results, evaluate software against specifications, document testing phases and defects, report bugs, troubleshoot issues, and perform post-release testing. Collaborate with developers and cross-functional teams to create test plans and maintain quality throughout the software development lifecycle. Basic database and SQL knowledge, JIRA, web application testing, STLC, regression testing, and test management software experience are required or preferred.

Develop scalable applications using .NET, .NET Core, Entity Framework, and React. Responsibilities include designing, developing, and consuming RESTful APIs; creating robust backend services; working with SQL or NoSQL databases; and supporting database design, optimization, and management. The role also requires effective communication and collaboration within a technical team.

Design, develop, test, and maintain scalable backend services, APIs, and distributed systems. Collaborate with product, frontend, and infrastructure teams; optimize performance, scalability, and reliability; write maintainable code; conduct code reviews; troubleshoot production issues; and contribute to system design and technical decisions.

Design, develop, test, integrate, document, troubleshoot, and upgrade software applications. The role involves participating in the software development lifecycle, solving complex problems, producing technical specifications, building APIs and services, and creating scalable, reliable solutions using Java, Spring Boot, SQL databases, Hibernate, and JPA.

Build and maintain end-to-end agentic web applications using React, Next.js, Streamlit, Node.js, and Python. Integrate LLM APIs, retrieval-augmented generation, embeddings, and fine-tuning workflows. Develop cloud-based agents and CI/CD pipelines, implement AI evaluation metrics and guardrails, and optimize applications for latency, cost, security, and quality. Translate business problems into AI-powered features while writing clean, tested, maintainable, and instrumented code.