Role & responsibilities
Job Summary
AI Product Engineer
product development mindset
The candidate should be comfortable working across the AI application stackfrom identifying suitable AI capabilities and integrating models to developing user-facing features, APIs, evaluation mechanisms, and production solutions.
Key Responsibilities
- Design, develop, and enhance
AI-powered products and applications
from concept through production. - Translate business requirements and user problems into practical AI-enabled product features.
- Work closely with Product Managers, UX/UI teams, architects, and business stakeholders to define and deliver AI capabilities.
- Integrate
LLMs, Generative AI models, AI APIs, and third-party AI services
into applications. - Develop AI-powered features such as intelligent assistants, document processing, summarization, classification, recommendation, search, content generation, and conversational experiences.
- Build and integrate
REST APIs and backend services
to support AI-driven applications. - Develop proof-of-concepts and rapidly convert successful prototypes into production-ready solutions.
- Implement
prompt engineering
, structured prompting, context management, and response optimization. - Develop and integrate
RAG-based solutions
using enterprise data and knowledge sources. - Work with vector databases, embeddings, semantic search, and retrieval mechanisms where applicable.
- Evaluate AI outputs for accuracy, relevance, consistency, latency, and cost.
- Implement appropriate guardrails and validation mechanisms to improve AI reliability and reduce hallucinations.
- Optimize AI applications for scalability, performance, security, and cost efficiency.
- Participate in product discovery, technical design, development, testing, deployment, and continuous improvement.
- Monitor production AI features and proactively identify opportunities for improvement.
- Stay current with emerging AI technologies and assess their applicability to product use cases.
Preferred Skills
- Experience with
LangChain, LlamaIndex, Semantic Kernel, or LangGraph
. - Exposure to
AI Agents / Agentic AI
, function calling, and tool-use patterns. - Experience with multimodal AI solutions involving text, images, documents, or audio.
- Exposure to LLM fine-tuning, LoRA/PEFT, or model customization.
- Knowledge of AI evaluation frameworks and LLM observability.
- Experience with OCR and intelligent document processing.
- Exposure to recommendation engines, semantic search, or intelligent automation.
- Experience working on SaaS or enterprise product development.
- Startup/product-company experience is an advantage.
Key Competencies
- AI Product Engineering
- Generative AI
- LLM Application Development
- RAG
- Prompt Engineering
- AI API Integration
- Python / Backend Development
- Vector Search & Embeddings
- AI Agents
- Product Thinking
- Rapid Prototyping
- API & Application Development
- AI Evaluation & Optimization
- Problem Solving
- Cross-functional Collaboration
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related discipline.
3+ years of professional software/application development experience
.- Demonstrable hands-on experience implementing
AI capabilities in real-world applications
. - Strong software engineering fundamentals combined with a genuine interest in AI technologies.
- Ability to work independently, take ownership, and operate effectively in a product-oriented environment.
Preferred candidate profile
Perks and benefits
