Vinove·1 day ago
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[DT] : AI Engineering / Lead
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What you'll do
- Partner directly with client engineering and business stakeholders to translate complex requirements into robust technical specifications and AI workflows.
- Architect and deploy production-grade AI solutions—including RAG pipelines, fine-tuned models, vector databases, and agent frameworks—within client cloud or on-premises environments.
- Lead integration efforts to connect AI microservices with existing enterprise infrastructure, legacy data stores, APIs, and business systems.
- Act as the primary technical authority and trusted advisor to client executive leadership (CTOs, VPs of Engineering) on AI strategy, security, and ROI.
- Design data orchestration pipelines, vector search infrastructure (e.g., Pinecone, Qdrant, Milvus, PGVector), and framework integrations (e.g., LangChain, LlamaIndex, Semantic Kernel).
- Optimize model latency, throughput, token usage, and computing costs (FinOps) for high-scale enterprise workloads.
- Implement robust evaluation, observability, and safety frameworks to monitor accuracy, drift, hallucinations, and security guardrails in production.
- Design distributed systems that meet enterprise compliance and regulatory standards (e.g., HIPAA, GDPR, SOC 2).
- Establish clean code bases, automated testing, CI/CD pipelines, and enterprise MLOps/LLMOps best practices.
- Troubleshoot performance bottlenecks, system outages, and deployment issues within customer environments.
- Conduct technical enablement and knowledge transfer sessions to empower client internal engineering teams.
- Oversee cross-functional delivery teams, mentor intermediate/junior engineers, conduct code reviews, and build reusable engineering playbooks.
What they're looking for
- 3–8+ years in software design, solution engineering, or systems architecture, including proven experience building and deploying production enterprise AI/ML solutions.
- Fluency in Python and modern software engineering practices.
- Expertise with modern AI stacks—PyTorch/TensorFlow, Hugging Face, OpenAI/Anthropic APIs, vector databases, prompt engineering, and agent frameworks.
- Deep hands-on experience with major cloud providers (AWS, Azure, or GCP), containerization (Docker), CI/CD, and infrastructure concepts.
- Outstanding technical communication and stakeholder management skills with the ability to explain complex AI trade-offs to non-technical business leaders.
Nice to have
- TypeScript/Node.js or Go
- Kubernetes/Terraform (preferred for Senior candidates)
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Job Family: AI Engineering / Lead
Level: Intermediate to Senior / Team Lead (Positioning and scope adapted based on experience)
Experience Guideline: 3–8+ years in software and systems engineering, with strong hands-on enterprise AI delivery and team/delivery leadership experience
Role Overview
As a Forward Deployed AI Engineer, you operate on the frontlines of innovation, bridging the gap between cutting-edge AI capabilities and enterprise production systems. You work directly at the intersection of client problem spaces and AI system architecture—designing, building, deploying, and scaling custom AI solutions (including LLM pipelines, RAG architectures, multi-agent workflows, and fine-tuned models) tailored to client environments.
Depending on experience level, you will either lead end-to-end client deliveries and mentor cross-functional teams (Senior/Team Lead) or drive key technical execution and client integration streams under strategic guidance (Intermediate).
Key Responsibilities : Client Engagement & Solution Delivery
- Partner directly with client engineering and business stakeholders to translate complex requirements into robust technical specifications and AI workflows.
- Architect and deploy production-grade AI solutions—including RAG pipelines, fine-tuned models, vector databases, and agent frameworks—within client cloud or on-premises environments.
- Lead integration efforts to connect AI microservices with existing enterprise infrastructure, legacy data stores, APIs, and business systems.
- (Senior/Team Lead) Act as the primary technical authority and trusted advisor to client executive leadership (CTOs, VPs of Engineering) on AI strategy, security, and ROI.
AI Engineering & System Architecture
- Design data orchestration pipelines, vector search infrastructure (e.g., Pinecone, Qdrant, Milvus, PGVector), and framework integrations (e.g., LangChain, LlamaIndex, Semantic Kernel).
- Optimize model latency, throughput, token usage, and computing costs (FinOps) for high-scale enterprise workloads.
- Implement robust evaluation, observability, and safety frameworks to monitor accuracy, drift, hallucinations, and security guardrails in production.
- (Senior/Team Lead) Design distributed systems that meet enterprise compliance and regulatory standards (e.g., HIPAA, GDPR, SOC 2).
Delivery Leadership, MLOps & Quality Assurance
- Establish clean code bases, automated testing, CI/CD pipelines, and enterprise MLOps/LLMOps best practices.
- Troubleshoot performance bottlenecks, system outages, and deployment issues within customer environments.
- Conduct technical enablement and knowledge transfer sessions to empower client internal engineering teams.
- (Senior/Team Lead) Oversee cross-functional delivery teams, mentor intermediate/junior engineers, conduct code reviews, and build reusable engineering playbooks.
Required Qualifications & Skills
- Experience: 3–8+ years in software design, solution engineering, or systems architecture, including proven experience building and deploying production enterprise AI/ML solutions.
- Core Languages: Fluency in Python and modern software engineering practices (TypeScript/Node.js or Go is a plus).
- AI Tech Stack: Expertise with modern AI stacks—PyTorch/TensorFlow, Hugging Face, OpenAI/Anthropic APIs, vector databases, prompt engineering, and agent frameworks.
- Cloud & DevOps: Deep hands-on experience with major cloud providers (AWS, Azure, or GCP), containerization (Docker), CI/CD, and infrastructure concepts (Kubernetes/Terraform preferred for Senior candidates).
- Communication: Outstanding technical communication and stakeholder management skills with the ability to explain complex AI trade-offs to non-technical business leaders.
- Preferred Certifications & Qualifications
Company
Vinove
Delhi NCR, India
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