This role is for one of our clients
Industry: Technology, Information and Internet
Seniority level: Mid-Senior level
Min Experience: 5 years
Location: Bangalore
JobType: full-time
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
We are looking for an AI Systems & Conversational Intelligence Architect to design and scale production-ready AI platforms that power intelligent, multilingual, and personalized user experiences. This role sits at the intersection of large language models, retrieval systems, and scalable ML infrastructure.
You will lead the development of high-performance conversational architectures, contextual knowledge engines, and personalization frameworks deployed in real-world, high-traffic environments. The ideal candidate brings strong machine learning foundations, deep LLM expertise, and a systems-thinking mindset to build reliable, efficient, and measurable AI products.
Key Responsibilities
Conversational Architecture & LLM Orchestration
Design scalable multi-turn conversational systems with contextual memory and adaptive personalization.
Build orchestration layers that manage prompts, routing logic, fallback strategies, and multi-model interactions.
Develop automated evaluation pipelines to measure response accuracy, factual consistency, tone alignment, and hallucination mitigation.
Optimize latency, throughput, and cost efficiency through batching, caching, and architectural refinements.
Retrieval & Context Engineering
Architect embedding-based search systems and vector retrieval pipelines.
Design end-to-end retrieval-augmented generation (RAG) workflows integrating structured and unstructured knowledge sources.
Improve contextual reasoning by combining semantic search, metadata filtering, and dynamic re-ranking strategies.
Personalization & Applied ML Systems
Build intelligent ranking, recommendation, and user segmentation pipelines.
Develop adaptive content generation systems tailored to user preferences and behavioral signals.
Fine-tune and adapt multilingual NLP/NLG systems, including support for Hindi and other regional languages.
Model Optimization & Production Deployment
Deploy open-source and proprietary LLMs in scalable, low-latency environments.
Evaluate trade-offs between performance, cost, and model complexity.
Implement MLOps best practices including versioning, monitoring, automated evaluation, and continuous improvement workflows.
Design cloud-native AI systems leveraging distributed compute, storage, and caching infrastructure (AWS preferred).
Workflow Automation & Infrastructure
Build robust ML pipelines using orchestration frameworks such as Airflow, Prefect, or Celery.
Integrate modern data stack components including relational databases, caching layers, object storage, and streaming systems.
Containerize and deploy services using Docker and scalable infrastructure patterns.
Candidate Profile
Required Experience
5+ years of experience building and deploying ML or NLP systems in production environments.
Strong Python expertise with hands-on experience in PyTorch, scikit-learn, HuggingFace, and LLM tooling ecosystems.
Demonstrated experience with embeddings, vector search systems, and RAG pipelines.
Proven ability to design scalable conversational AI systems with dialog management and context handling.
Strong understanding of distributed system design and cloud-based deployments.
Preferred Experience
Experience developing multilingual conversational agents or recommendation systems.
Exposure to advanced fine-tuning methods such as LoRA, PEFT, RLHF, or prompt optimization strategies.
Contributions to open-source AI/LLM projects.
Experience working on culturally nuanced or content-rich AI applications.
What You'll Gain
Ownership of foundational AI systems shaping large-scale conversational experiences.
Opportunity to solve complex multilingual and personalization challenges using frontier LLM technologies.
Collaboration with cross-functional teams spanning product, engineering, and domain expertise.
Long-term growth opportunities in AI architecture and platform leadership.
Core Competencies
Conversational AI Architecture
- Large Language Models
- Retrieval-Augmented Generation
- Multilingual NLP
- Personalization Systems
- Vector Search
- Model Deployment
- MLOps
- Cloud-Native AI Infrastructure
- Scalable ML Engineering
