Top Tech Jobs & Startup Jobs

Designs and delivers production-grade generative AI solutions using LLMs, RAG, agentic workflows, and Azure services. Responsibilities include architecture, development, testing, deployment, evaluation, observability, guardrails, cost optimization, and maintenance. The role translates business problems into measurable AI outcomes, develops reusable frameworks, collaborates with stakeholders, contributes to proposals and proofs of concept, and supports responsible AI practices, knowledge sharing, code reviews, and mentorship.

Deploys and operates Neo4j Enterprise on Azure Kubernetes Service, including clustering, disaster recovery, replication, failover, monitoring, and capacity tuning. Builds Terraform infrastructure, CI/CD pipelines, artifact repositories, network isolation, and secure connectivity. Executes approved ontology changes, manages graph deployment workflows, and provides production on-call support for mission-critical infrastructure.

Own strategic growth and commercial outcomes across Consumer Packaged Goods accounts, particularly Global Capability Centers. Build executive relationships, identify opportunities, lead consultative sales campaigns, shape AI and data transformation solutions, and coordinate consulting, analytics, engineering, and capability teams. The role requires strong CPG analytics expertise, pre-sales experience, account management skills, and the ability to engage technical and executive stakeholders while managing revenue and margin targets.

Owns end-to-end platform architecture across backend, frontend, AI services, data, cloud infrastructure, security, observability, and integrations. Designs scalable microservices using Java and Spring Boot, guides AI and frontend development, establishes CI/CD and architecture standards, and leads HRMS/ATS integrations. Represents engineering in client and pre-sales discussions, translates requirements into technical solutions, and mentors engineers across multiple teams.

Designs, deploys, and maintains secure, scalable cloud platforms supporting data and analytics workloads. Responsibilities include infrastructure automation with Infrastructure as Code, cloud-native platform development, deployment pipelines, monitoring, incident response, vulnerability remediation, cost optimization, documentation, and technical guidance. The role collaborates with engineering, security, infrastructure, and operations teams while establishing platform standards and mentoring engineers.

Own end-to-end solution and application architecture for a LangGraph-based HR agentic AI platform on Azure. Design cloud-native infrastructure, enterprise ERP and HR integrations, scalable and fault-tolerant systems, API and event-driven patterns, and deployment strategies. Govern data science and engineering teams, lead architecture workshops, and provide technical sign-offs. The role requires hands-on Azure architecture, enterprise integration experience, and exposure to generative AI or agentic systems.

Lead the architecture and hands-on development of AI agents that execute end-to-end payment transactions across multiple markets. Design agent orchestration, transaction workflows, integrations, security controls, exception handling, and deterministic payment logic. Build primarily with Python and LLMs, integrate payment platforms and enterprise services, lead testing and transaction validation, and mentor engineers. Requires deep software architecture, payments, distributed systems, cloud, and AI agent engineering expertise.

Develop and execute automated quality assurance for data products and Generative AI platforms. Validate data migration, ETL, data warehouses, ingestion, mappings, reconciliations, and incremental loads using SQL, Python, pytest, and dataframes. Build test cases, automation frameworks, and validation scripts; manage defects; contribute to load testing; and collaborate with engineering, support, product, and business teams to maintain quality across the data lifecycle.

Leads end-to-end architecture for enterprise applications and data platforms across Azure, Snowflake, application engineering, data, integration, and AI. Designs secure, scalable cloud solutions involving data warehouses, pipelines, semantic layers, APIs, microservices, DevOps, and systems integration. Requires hands-on expertise with Azure, Snowflake, React, FastAPI, MongoDB, Kubernetes, and CI/CD. Preferred experience includes agentic AI, SAP integrations, finance automation, and data product design.

Develop NLP and generative AI models for text classification, sentiment analysis, entity recognition, translation, and semantic analysis. Responsibilities include text preprocessing, feature engineering, model training and evaluation, exploratory data analysis, algorithm optimization, and large-scale data processing. Collaborate with engineering and domain teams to integrate solutions, document methodologies, and apply emerging NLP and machine learning techniques.