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Technical Project Manager

Job in 400001, Mumbai, Maharashtra, India
Listing for: Straive
Full Time position
Listed on 2026-07-03
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software)
Job Description & How to Apply Below
Key Responsibilities:

Project Management     Own the end-to-end delivery of Generative AI projects — from requirements gathering to deployment and adoption.
Collaborate with data scientists, ML engineers, prompt engineers, and product managers to design scalable AI-powered solutions.
Evaluate, select, and integrate LLM platforms and APIs (e.g., GPT-5, Claude, Gemini, Mistral, LLaMA) into enterprise applications.
Define and manage project plans, timelines, budgets, and resource allocations.
Oversee prompt engineering, model fine-tuning, and RAG (Retrieval-Augmented
Generation) implementations for production-grade use cases.
Ensure AI solutions meet security, compliance, and ethical AI standards, including data privacy and bias mitigation.
Drive performance monitoring and evaluation of deployed AI models, ensuring they meet agreed SLAs.
Liaise with business stakeholders to translate high-level goals into actionable technical requirements.
Create risk management and contingency plans specific to AI system deployment and scaling.
Stay up to date with Gen-AI trends, research breakthroughs, and tool advancements, and proactively bring innovative ideas to the table.

You will have the following qualifications:
Project Management Expertise:
Strong track record managing technical projects in Agile/Scrum or hybrid delivery models.
Generative AI Implementation:
Hands-on experience integrating and deploying solutions using GPT-4/5, Claude, Gemini, or equivalent LLMs.
Architecture Understanding:
Familiarity with transformer-based architectures, embeddings, vector databases (Pinecone, Weaviate, FAISS), and API integrations.
Prompt Engineering

Skills:

Experience designing optimized prompts, context windows, and fine-tuned conversational flows.
RAG Pipelines:
Understanding of retrieval-augmented generation for knowledge-base-enhanced LLM outputs.
Cloud & Dev Ops:
Knowledge of AWS, Azure, or GCP AI/ML services; CI/CD pipelines for AI workloads.
Data Security & Compliance:
Knowledge of enterprise security standards, GDPR, SOC 2, HIPAA (as relevant).
Stakeholder Communication:
Exceptional ability to convey technical details to non-technical stakeholders.
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