Key Responsibilities
- Design, develop, and deploy scalable AI/ML solutions for business and customer-facing applications.
- Build Agentic AI / AI agent based applications.
- Develop and integrate solutions using Generative AI, Large Language Models (LLMs), NLP, and Machine Learning.
- Build intelligent applications using RAG, embeddings, vector search, prompt engineering, tool/function calling, and AI agents.
- Research, evaluate, and select appropriate models, frameworks, and technologies based on business and technical requirements.
- Develop efficient data and ML pipelines for data preparation, experimentation, model evaluation, deployment, and monitoring.
- Integrate AI/ML models with existing products, APIs, databases, and backend systems.
- Evaluate model performance and continuously improve accuracy, relevance, latency, reliability, and cost efficiency.
- Implement appropriate AI evaluation, monitoring, logging, and guardrails for production systems.
- Work closely with Product, Engineering, Data, and Business teams to understand requirements and translate them into practical AI solutions.
- Take end-to-end ownership of AI/ML projects from proof of concept to production deployment and continuous improvement.
- Identify opportunities to apply AI/ML to improve products, processes, automation, and customer experiences.
- Contribute to technical architecture, code quality, documentation, and engineering best practices.
- Mentor and support junior engineers while contributing to a collaborative engineering environment.
Required Skills & Experience
- 3 years of professional hands on experience in AI/ML engineering or a related field.
- Strong hands-on programming experience with Python, SQL.
- Strong understanding of Machine Learning, Deep Learning, NLP, and Generative AI.
- Practical experience working with LLMs and AI APIs such as OpenAI, Anthropic, Gemini, Hugging Face, or equivalent technologies.
- Hands-on experience developing RAG / agentic AI based applications, including embeddings, retrieval, vector search, and evaluation.
- Strong understanding of prompt engineering, structured outputs, function/tool calling, and AI agents.
- Experience building and deploying production-grade AI/ML applications.
- Good understanding of REST APIs, databases, backend systems, and scalable application architecture.
- Experience with vector databases such as Pinecone, Qdrant, Weaviate, Milvus, pgvector, or equivalent.
- Familiarity with cloud platforms such as AWS, GCP, or Azure.
- Experience with Docker, CI/CD, deployment, monitoring, and production engineering practices.
- Good understanding of model evaluation, experimentation, and performance optimization.
- Strong analytical, problem-solving, and debugging skills.
What We Are Looking For
- Strong ownership and the ability to independently drive AI/ML projects.
- A practical approach to applying AI to real-world business problems.
- Strong combination of AI/ML expertise and software engineering fundamentals.
- Ability to take solutions from experimentation to stable, scalable production systems.
- Strong focus on quality, performance, scalability, security, and cost efficiency.
- Curiosity and willingness to stay updated with the rapidly evolving AI/ML ecosystem.
- Strong communication and collaboration skills.
- Ability to work effectively in a fast-paced and evolving technology environment.
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