Shared job[DT] : AI Engineering / Lead
Noida · Staff/Principal
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Job description
- 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.
- 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).
- 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.
- 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
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