Key Responsibilities :

- Design, develop, and deploy AI and machine learning solutions for business applications.

- Build and integrate generative AI applications using large language models (LLMs).

- Develop AI-powered workflows, intelligent automation solutions, and AI-driven applications.

- Work with frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face.

- Develop prompt engineering strategies and optimize LLM outputs for accuracy and performance.

- Build Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases.

- Integrate AI models with APIs, databases, and enterprise applications.

- Perform data preprocessing, feature engineering, model evaluation, and performance optimization.

- Collaborate with product managers, software engineers, and data teams to translate business requirements into AI solutions.

- Monitor deployed models and troubleshoot issues related to accuracy, latency, scalability, and reliability.

- Implement best practices for responsible AI, data privacy, security, and model governance.

- Stay updated with emerging AI technologies, frameworks, and industry developments.

Required Skills & Experience :

- 3+ years of experience in AI/ML engineering, software development, or a related field.

- Strong programming skills in Python.

- Hands-on experience developing and deploying machine learning or AI-based applications.

- Understanding of machine learning algorithms, model training, evaluation, and optimization.

- Experience with generative AI and large language models (LLMs).

- Familiarity with LLM APIs and frameworks such as OpenAI, Anthropic, LangChain, or LlamaIndex.

- Experience building REST APIs and integrating AI solutions into applications.

- Knowledge of data processing, model evaluation, and AI application performance optimization.

- Understanding of databases, data pipelines, and software development best practices.

- Strong analytical, problem-solving, and communication skills.

Good to Have :

- Experience building Retrieval-Augmented Generation (RAG) applications.

- Familiarity with vector databases such as Pinecone, Weaviate, or FAISS.

- Knowledge of AI agents, multi-agent systems, and workflow orchestration.

- Experience with cloud platforms such as AWS, Azure, or Google Cloud.

- Familiarity with Docker, Kubernetes, and CI/CD pipelines.

- Experience with MLOps, model monitoring, and production deployment.

- Knowledge of fine-tuning, embeddings, and evaluation techniques for LLMs.

Preferred Candidate Profile :

- Ability to convert business requirements into practical and scalable AI solutions.

- Strong understanding of AI system architecture and integration patterns.

- Experience working in Agile development environments.

- A continuous-learning mindset with an interest in emerging AI technologies.