AI Architect
Wishtree Technologies
- Location
- Pune District, Maharashtra, India
- Job type
- Full-time
Required skills
- LangChain
- Python
- AWS
- Azure
- communication skills
- data science
- Docker
- end-to-end
- GCP
- Kubernetes
- machine learning
- NLP
- product improvements
- TensorFlow
- Pytorch
- Vertex
About the role
Wishtree Technologies
Website:
wishtreetech.com
Job details:
- Overall 8+ years of experience in Design end-to-end AI/ML architectures for enterprise-scale Generative AI products, including LLM-based applications, RAG pipelines, and agentic workflows.
- Hands on Conversational AI agents, RAG(AS), NLP
- Lead the selection, fine-tuning, and deployment of large language models (LLMs) such as GPT-4, Claude, Llama, and Mistral.
- Architect robust data pipelines and vector databases (Pinecone, Weaviate, pgvector, Qdrant) to support retrieval-augmented generation (RAG) and embedding-based search.
- Define AI governance, responsible AI principles, and model observability frameworks across the organization.
- Collaborate with sales, engineering, and data science teams to translate business requirements into AI solutions.
- Evaluate and integrate third-party AI APIs, MLOps platforms, and cloud AI services (AWS SageMaker, Azure OpenAI, GCP Vertex AI).
- Mentor junior AI engineers and data scientists; champion best practices in model development and deployment.
- Stay current with research in foundation models, multi-modal AI, and emerging GenAI techniques, and translate insights into actionable product improvements.
- Lead proof-of-concept (PoC) projects and drive them to production-grade deployments with a focus on latency, cost, and reliability.
- Ensure AI systems comply with data privacy regulations (GDPR, DPDP) and internal security policies.
Required Qualifications
- 2+ years of hands-on experience in Generative AI and Machine Learning in a production environment.
- Proficiency in Python and ML frameworks: PyTorch, TensorFlow, Hugging Face Transformers.
- Proven experience building and deploying LLM-based applications (prompt engineering, fine-tuning, RLHF).
- Strong knowledge of RAG architectures, embedding models, and vector databases.
- Experience with cloud platforms (AWS, Azure, or GCP) and containerisation (Docker, Kubernetes).
- Solid understanding of MLOps practices: CI/CD for ML, model versioning, monitoring, and drift detection.
- Familiarity with orchestration frameworks such as LangChain, LlamaIndex, or AutoGen.
- Strong system design skills with the ability to architect scalable, fault-tolerant AI systems.
- Excellent communication skills to present technical concepts to non-technical stakeholders.
- Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field (or equivalent practical experience).
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