Top Tech Jobs & Startup Jobs

Lead strategy, roadmap, development, and growth for an agentic AI platform that automates complex enterprise workflows. Conduct customer and market discovery, translate business pain points into scalable features, prioritize backlogs, and coordinate agile delivery with engineering, AI/ML, design, sales, and marketing teams. Establish AI governance, monitoring, compliance, and ethical performance frameworks while supporting product demos, enablement, adoption, and revenue growth.

Build and own production-grade AI systems, including backend services, scalable APIs, LLM integrations, RAG pipelines, and agentic workflows. Responsibilities include testing, documentation, model deployment, performance optimization, collaboration, and continuous learning, with increasing ownership and complexity based on experience.

Leads the design, development, and delivery of scalable enterprise full-stack applications using Python, FastAPI, Java, Spring Boot, React, Angular, mobile technologies, and Azure. Builds APIs and microservices, supports CI/CD, contributes to architecture and roadmaps, troubleshoots production issues, and ensures secure, maintainable performance. The role also leads code reviews, collaborates with stakeholders, documents technical solutions, and mentors distributed engineering teams.

Drive AI research strategy and build scalable NLP and Generative AI solutions. Develop prompt engineering, fine-tuning, RAG, model evaluation, experimentation, and optimization pipelines while collaborating with AI Engineering teams. The role emphasizes scientific thinking, reproducibility, ownership, technical leadership, continuous innovation, and progressively increasing responsibility.

Build production-grade AI systems by developing scalable APIs, integrating LLMs, contributing to RAG pipelines and agentic workflows, writing clean Python, testing, documenting, supporting deployments, optimizing performance, and collaborating with engineering teams. Responsibilities grow in ownership and complexity with experience while emphasizing software engineering best practices, continuous learning, code quality, and a production mindset.

Architect scalable, low-latency AI/ML systems and real-time inference pipelines for voice, vision, and NLP applications. Lead model productionization, infrastructure and framework selection, architecture reviews, optimization, observability, and MLOps best practices. Collaborate with product, data science, and engineering teams while mentoring engineers. The role requires expertise in distributed systems, LLM inference optimization, vector databases, RAG, containerization, cloud platforms, CI/CD, edge deployment, and software architecture.

Lead development of enterprise NLP and generative AI solutions, including prompt engineering, fine-tuning, retrieval-augmented generation, model evaluation, experimentation, and optimization. Collaborate with AI Engineering teams, promote reproducible scientific practices, continuously innovate, and mentor others while assuming greater ownership with experience.

Develop and improve NLP and generative AI solutions using prompt engineering, fine-tuning, retrieval-augmented generation, dataset preparation, model evaluation, experimentation, and optimization. Collaborate with AI Engineering teams, promote reproducibility, and progressively take on greater ownership while supporting continuous innovation.

Lead strategy, vision, development, and growth for an agentic AI platform that automates complex enterprise tasks. Responsibilities include customer and market discovery, competitor analysis, feature prioritization, backlog and roadmap management, agile delivery, and collaboration with engineering, AI/ML, design, sales, marketing, and solutions teams. The role also supports product demos, enablement, client presentations, adoption, and revenue growth.

Develop and improve NLP and generative AI solutions through prompt engineering, model fine-tuning, RAG pipelines, evaluation, experimentation, and optimization. Collaborate with AI Engineering teams, promote reproducibility, and progressively take on greater ownership while driving continuous innovation.