Job Description
We are looking for a Data Scientist / AI Engineer who can combine strong Python programming skills, machine learning expertise, and modern Generative AI technologies to develop and deploy production-ready AI solutions. The ideal candidate has hands-on experience delivering end-to-end AI projects, particularly in LLMs, RAG architectures, and AI-powered applications, and can collaborate effectively with technical and business teams.
Key Responsibilities
- Design, develop, and deploy AI and machine learning solutions for real-world business problems.
- Build LLM-based applications using Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, and vector databases.
- Develop and optimize AI pipelines, data processing workflows, and model inference services using Python.
- Take AI solutions from experimentation and prototyping to production deployment, monitoring, and continuous improvement.
- Evaluate model performance, accuracy, latency, scalability, and cost to ensure reliable production use.
- Work with structured and unstructured data to develop predictive models, NLP solutions, and intelligent applications.
- Collaborate with software engineers, data engineers, product managers, and business stakeholders throughout the project lifecycle.
- Translate business requirements into practical AI solutions and communicate technical concepts clearly to both technical and non-technical audiences.
- Follow best practices for version control, testing, API development, documentation, and maintainable code.
Requirements
- Strong hands-on experience with Python and its data science and machine learning ecosystem.
- More than 5 years of experience in the related field
- Experience developing and deploying AI/ML solutions in production environments.
- Practical knowledge of LLMs, Generative AI, RAG architectures, embeddings, and prompt engineering.
- Experience with machine learning algorithms, model training, evaluation, and optimization.
- Familiarity with frameworks and libraries such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain, or LlamaIndex.
- Experience with vector databases and semantic search technologies such as FAISS, Pinecone, Weaviate, or Chroma.
- Experience developing REST APIs and integrating AI models into software applications.
- Understanding of data preparation, feature engineering, model evaluation, and data processing pipelines.
- Strong communication and problem-solving skills, with the ability to explain technical findings and recommendations clearly.
- Proven ability to take ownership of projects, work collaboratively, and deliver solutions from concept to production.
Nice to Have
- Experience with AI agents, tool calling, and multi-step LLM workflows.
- Knowledge of cloud platforms such as AWS, Azure, or Google Cloud.
- Experience with Docker, Kubernetes, CI/CD, model serving, and MLOps practices.
- Familiarity with LLM evaluation, hallucination reduction, guardrails, and observability.
- Experience with NLP, information retrieval, recommendation systems, or predictive analytics.
- Experience working directly with clients or business stakeholders to define requirements and present project outcomes.
Ideal Candidate Profile
We are looking for someone who combines the analytical mindset of a Data Scientist with the engineering capabilities of an AI Engineer. The ideal candidate has delivered real-world AI projects, understands the challenges of production environments, keeps up with emerging LLM and RAG technologies, and communicates effectively with both technical teams and business stakeholders.