🛠️ Required Skills
✅ 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
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
🎓 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
Job description provided by EXL Service.
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