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Gen AI Architect

exl Service.com (India) Private Limited
Noida, IND
Full-time
Mid · 5+ years experience
Hybrid
Discovered 1 weeks ago
awsazuregcplangchainpythonpytorch

Full Job Posting

✅ Technical Skills

  • Strong knowledge of:
  • Machine Learning & Deep Learning
  • Large Language Models (LLMs)
  • NLP (Natural Language Processing)
  • Experience with:
  • Python, APIs, microservices
  • AI frameworks (TensorFlow, PyTorch, LangChain)
  • Cloud platforms:
  • Azure / AWS / GCP
  • ✅ GenAI-Specific Skills
  • Prompt engineering
  • RAG architectures
  • Fine-tuning LLMs
  • Vector databases (e.g., Pinecone, FAISS)
  • ✅ Soft Skills
  • Problem-solving mindset
  • Strong communication (explaining AI to non-technical teams)
  • Strategic thinking

🎓 Qualifications

  • Bachelor’s / Master’s in Computer Science, AI, Data Science, or related field
  • 5–12+ years in software engineering, ML, or AI roles
  • Experience with enterprise-level systems
  • ✅
  • Machine Learning & Deep Learning
  • Large Language Models (LLMs)
  • NLP (Natural Language Processing)
  • Python, APIs, microservices
  • AI frameworks (TensorFlow, PyTorch, LangChain)
  • Azure / AWS / GCP
  • ✅ GenAI-Specific Skills
  • Prompt engineering
  • RAG architectures
  • Fine-tuning LLMs
  • Vector databases (e.g., Pinecone, FAISS)
  • ✅ Soft Skills
  • Problem-solving mindset
  • Strong communication (explaining AI to non-technical teams)
  • Strategic thinking

Key Responsibilities

  • Solution Architecture & Design
  • Design end-to-end Generative AI solutions using LLMs (like GPT, Gemini, etc.)
  • Define system architecture (APIs, pipelines, integrations)
  • Choose appropriate models, frameworks, and cloud platforms
  • 🔹 2. Model Selection & Optimization
  • Evaluate and select foundation models / LLMs
  • Fine-tune or customize models using:
  • Prompt engineering
  • Retrieval-Augmented Generation (RAG)
  • Fine-tuning datasets
  • 🔹 3. Integration & Deployment
  • Integrate GenAI solutions into enterprise systems (CRM, ERP, apps)
  • Deploy solutions using cloud platforms like:
  • Azure AI / OpenAI
  • AWS Bedrock
  • Google Vertex AI
  • 🔹 4. Data & AI Governance
  • Ensure data privacy, security, and compliance
  • Implement responsible AI practices (bias mitigation, explainability)
  • Monitor model performance and accuracy
  • 🔹 5. Collaboration & Leadership
  • Work with business stakeholders to identify GenAI use cases
  • Guide engineering teams on implementation
  • Act as a technical thought leader in AI adoption

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