Experience: 3.00 + years
Salary: Confidential (based on experience)
Expected Notice Period: 15 Days
Shift: (GMT+05:30) Asia/Kolkata (IST)
Opportunity Type: Remote
Placement Type: Full Time Permanent position(Payroll and Compliance to be managed by: Qant)
(*Note: This is a requirement for one of Uplers client - Qant)
What do you need for this opportunity
Must have skills required:
LangChain, NoSQL databases, Terraform, LLM, MCP, rag, Solutions Architecture, Python
Qant is Looking for:
We're building the intelligence layer that connects top investment funds WITH_REPLACED the external data that drives their investment research. We sit between where information is stored and where it gets acted on, filtering and surfacing what's relevant to their use cases pre vendor trials. We're looking for a
Solutions Architect who can design and deliver the AI infrastructure that makes that possible, including RAG pipelines, agentic workflows, and LLM-powered research tools deployed within strict enterprise security requirements.
Responsibilities:
- Architect end-to-end generative AI solutions: design RAG pipelines, agentic workflows, and LLM-powered features that solve real customer problems within enterprise security boundaries.
- Define system architecture for scalable, secure backend services (Python, FastAPI) supporting AI workloads, from retrieval systems to agent orchestration.
- Design and optimize LLM deployment strategies: prompt engineering, retrieval grounding, evaluation frameworks, and model routing for production reliability.
- Establish patterns for agentic AI systems: multi-step reasoning, tool use, memory management, and workflow orchestration aligned WITH_REPLACED on-prem constraints.
- Lead technical evaluation and integration of vector databases, embedding models, and alternative retrieval architectures.
- Harden Linux (Debian) environments and configure Nginx for API gateways serving AI services WITH_REPLACED appropriate rate limiting and security controls.
- Mentor engineering teams on AI architecture patterns, best practices, and security-first design principles.
- Navigate ambiguity: translate customer needs into technical architecture, communicate trade-offs clearly, and drive incremental delivery.
Required Experience:
- 2+ years production experience WITH_REPLACED LLMs and RAG systems: prompt engineering, retrieval pipelines, grounding techniques, evaluation frameworks, vector stores or alternatives.
- 2+ years designing or implementing agentic AI systems: multi-step reasoning, tool calling, agent orchestration, or adjacent autonomous capabilities.
- Advanced Python expertise (typing, async, performance optimization, AI/ML libraries).
- Solutions architecture experience: designing distributed systems, API strategies, and data flows at scale.
- Production REST APIs WITH_REPLACED FastAPI (OpenAPI, async I/O, authentication, observability).
- Strong hands-on WITH_REPLACED pandas, NumPy, SciPy, and scientific Python ecosystem.
- Linux (Debian) system administration and Nginx configuration.
- Proven ability to lead technical strategy and mentor engineering teams.
- Clear communicator who thrives under pressure and ships iteratively.
Preferred Experience:
- Enterprise AI deployments WITH_REPLACED strict data governance, on-prem constraints, or air-gapped environments.
- Experience WITH_REPLACED agent frameworks (LangChain, LlamaIndex, AutoGPT, Crew AI, or custom orchestration).
- Fine-tuning, model evaluation, or working WITH_REPLACED local/open-source LLMs.
- High-speed NoSQL databases (LMDB, Redis), vector databases (Pinecone, Weaviate, Milvus, ChromaDB), or alternative retrieval systems.
- Containers/CI-CD (Docker, GitHub Actions), infrastructure-as-code (Terraform).
- Performance profiling and optimization for AI workloads.
Our Benefits:
- Competitive Compensation: Attractive salary package WITH_REPLACED equity.
- High ownership and direct impact on projects that influence investment decision-making processes at top global investment funds.
- Professional Growth: Access to the latest tools and tech in data science and finance, WITH_REPLACED abundant learning opportunities.
- Flexible hours and remote work options in a supportive and inclusive company culture.
About Us
We've centralised data discovery and utilisation engine for capital markets. It connects front office teams WITH_REPLACED statistically validated data relevant to their specific research and investment use cases prior to initiating vendor trials. The platform identifies highsignal data across both discretionary and quantitative strategies, so investors can validate ideas, refine models and uncover new alpha. We support large-scale data discovery while maintaining intellectual property protection for both investment firms and data providers.
How to apply for this opportunity
- Step 1: Click On Apply! And Register or Login on our portal.
- Step 2: Complete the Screening Form & Upload updated Resume
- Step 3: Increase your chances to get shortlisted & meet the client for the Interview!
About Uplers:
Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.
(Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).
So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!
