[Hiring] AI Engineer @Volga Partners

Role Description

The AI Engineer at Volga Partners will design, develop, and deploy production-ready Generative AI and LLM-powered applications, including RAG systems, AI assistants, intelligent automation tools, and agentic workflows. This is a hands-on engineering role requiring strong Python skills and practical experience with LLMs, embeddings, vector databases, AI APIs, and modern AI frameworks. The AI Engineer will work closely with engineering, product, operations, and leadership teams to deliver scalable AI solutions for global clients.

Key Responsibilities

• Design, build, and deploy production-ready Generative AI and LLM-powered applications.
• Develop chatbots, copilots, intelligent automation tools, and AI-assisted workflows.
• Build and optimize RAG pipelines using embeddings, vector search, and structured or unstructured data.
• Integrate LLM platforms such as OpenAI, Anthropic, Azure OpenAI, and similar providers.
• Develop backend services and APIs using Python, FastAPI, Flask, or similar frameworks.
• Design prompts, system instructions, and context-management strategies to improve model performance and reliability.
• Build scalable AI services that can move from prototypes into production.
• Evaluate LLM outputs and improve quality, accuracy, latency, reliability, and cost.
• Monitor, troubleshoot, and optimize deployed AI systems.
• Work with vector databases and search technologies for semantic retrieval and knowledge-based applications.
• Implement appropriate evaluation, validation, guardrails, and error-handling strategies.
• Identify and resolve production issues and system regressions.
• Partner with engineering, product, operations, and business teams to translate requirements into practical AI solutions.
• Contribute to architecture decisions, technical discussions, and documentation.
• Stay current with developments in Generative AI, LLMs, RAG, agentic AI, and emerging AI tooling.
• Evaluate and implement new AI models, frameworks, and techniques where appropriate.

Qualifications

• 3+ years of software engineering, AI/ML engineering, or related development experience.
• Strong proficiency in Python.
• Practical experience building applications using LLMs.
• Hands-on experience with RAG, embeddings, semantic search, and vector databases.
• Experience integrating LLM APIs such as OpenAI, Anthropic, Azure OpenAI, or similar platforms.
• Understanding of prompt engineering, model behavior, context management, and LLM evaluation.
• Experience developing APIs or backend services using FastAPI, Flask, or similar frameworks.
• Strong software engineering fundamentals, including Git, REST APIs, testing, debugging, and system design.
• Familiarity with Docker and modern development practices.
• Ability to take an AI use case from requirements through development, testing, and deployment.
• Strong problem-solving skills and written/verbal English communication.
• Ability to work effectively in a fully remote, globally distributed team.

Preferred Qualifications

• Experience building production AI copilots, chatbots, agents, or intelligent automation systems.
• Experience with LangChain, LlamaIndex, LangGraph, or similar AI orchestration frameworks.
• Experience with Pinecone, Weaviate, Qdrant, FAISS, Chroma, or similar vector databases.
• Familiarity with agentic workflows, tool/function calling, and multi-step AI systems.
• Experience with LLM fine-tuning, LoRA, or similar techniques.
• Experience deploying AI applications in AWS, Azure, or GCP.
• Familiarity with model monitoring, evaluation frameworks, guardrails, and responsible AI practices.
• Experience with SQL, CI/CD, Kubernetes, or model-serving infrastructure.

Working Relationships

• Engineering leadership and product teams.
• Software engineers and backend developers.
• Global AI research and development teams.
• Product, operations, and business stakeholders.

Success in This Role

Success means you can build with AI and deliver reliable production solutions, not simply experiment with AI models. You should be able to:

• Build and deploy practical Generative AI and LLM applications.
• Demonstrate real-world experience with RAG, embeddings, vector search, and LLM APIs.
• Explain the architecture and technical decisions behind your work.
• Translate emerging AI technologies into practical engineering solutions.
• Improve AI system quality, reliability, latency, and cost.
• Work effectively with distributed global teams and adapt to evolving priorities.

Compensation & Benefits

• Annual Compensation: ₹15,00,000-₹22,00,000 (15-22 LPA), based on experience and technical expertise.
• Contract and payment administration facilitated through Deel.
• 90-day probationary period.
• Benefits eligibility upon successful completion of probation, including:
• Paid Time Off (PTO) in accordance with company policy.
• Holiday pay for company-observed holidays.
• Career growth and professional development opportunities.

Work Environment

This is a fully remote position based in India. Candidates should be comfortable working independently and collaborating with globally distributed teams across different time zones.

Required Work Schedule

This AI Engineer role requires working Monday through Friday, 7:00 AM–4:00 PM Pacific Time (PT). For team members based in India, this is an overnight schedule and will generally correspond to approximately 7:30 PM–4:30 AM IST or 8:30 PM–5:30 AM IST, depending on U.S. daylight saving time. The ability to consistently work the full Pacific Time schedule is a required and non-negotiable condition of this role. Candidates must be comfortable working overnight hours in India and maintaining reliable availability throughout the assigned shift. Due to the nature of AI engineering work, project deadlines, production needs, client requirements, or other business priorities may occasionally require weekend availability or schedule adjustments. Weekend work is not intended to be the standard weekly schedule, but flexibility to support business needs when required is an expectation of the role.

Our Hiring Process

• Initial Recruiter Screening: An introductory video call to discuss your experience, career goals, salary expectations, and the role.
• Practical Skills Assessment: A take-home assessment evaluating technical knowledge, problem-solving, and your approach to a real-world Generative AI/LLM engineering scenario.
• Assessment Review: Our team will evaluate your submission based on technical approach, architecture, implementation, and production readiness.
• Final Interview – CTO: A technical discussion focused on your assessment, architectural thinking, problem-solving, Generative AI/LLM experience, and overall alignment with the role.