AI Engineer

We are looking for a hands-on AI Engineer to build production-ready AI solutions across Machine Learning and Generative AI use cases. The role focuses on implementation and technical delivery: developing LLM-powered applications building ML models integrating AI capabilities into enterprise systems and improving the reliability performance and maintainability of deployed solutions.

Responsibilities  

• Develop and maintain AI solutions using both Machine Learning and Generative AI approaches aligned with business and client needs.
• Build and improve LLM-based applications including prompt engineering orchestration flows and tool usage.
• Implement and optimize RAG pipelines including chunking embeddings ranking and retrieval strategies.
• Work with vector databases such as pgvector FAISS Pinecone or Weaviate to support semantic search and context-aware responses.
• Develop integrations using MCP or similar approaches to connect AI systems with external tools APIs and enterprise platforms.
• Train evaluate and deploy ML models for prediction classification clustering or other data-driven use cases.
• Contribute to API development and integration of AI capabilities into existing applications and workflows.
• Support deployment monitoring and optimization of AI services in cloud and/or on-premise environments.
• Implement basic guardrails validation and monitoring mechanisms to improve quality reliability and safe usage of AI systems.
• Deploy configure and support AI workloads in Azure AWS or GCP using cloud services for compute storage networking security and monitoring.
• Collaborate with cross-functional teams including software engineers data scientists and business stakeholders to deliver production-ready solutions.
• Stay up to date with relevant developments in AI ML and GenAI and contribute to knowledge sharing within the team.

Qualifications :

Must have:

• 2 - 4 years of experience in Python development including libraries such as Pandas NumPy and Scikit-learn.
• Hands-on experience building and deploying Machine Learning models in real-world use cases.
• Practical experience with LLM-based applications including prompt engineering and LLM integration.
• Experience with frameworks such as LangChain or similar orchestration tools.
• Experience designing or implementing RAG pipelines including document chunking embeddings and retrieval strategies.
• Hands-on experience with vector databases such as pgvector FAISS Pinecone or Weaviate.
• Good understanding of embeddings semantic search and information retrieval concepts.
• Experience with MCP or similar tool-integration approaches for AI systems.
• Experience building and consuming APIs for system integration.
• Hands-on experience deploying and operating AI/ML services in at least one major cloud platform: Azure AWS or GCP.
• Working knowledge of cloud platforms and/or on-premise deployment models.
• Understanding of logging monitoring and observability fundamentals.
• Good communication skills and ability to work effectively in cross-functional teams.

Nice to have:

• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Familiarity with MLOps tools and practices such as MLflow Airflow or Kubeflow.
• Exposure to AI safety guardrails or evaluation frameworks for LLM-based systems.
• Experience with backend technologies such as Java or Spring.
• Experience with frontend technologies for AI-enabled applications.
• Familiarity with real-time or streaming AI/ML systems.
• Experience working with enterprise data platforms or data engineering pipelines.

Additional Information :

At Accesa you can

Enjoy our holistic benefits program that covers the four pillars that we believe come together to support our wellbeing covering social physical emotional wellbeing as well as work-life fusion.

• Physical Wellbeing: Our wellbeing program includes medical benefits gym support and personalised fitness options for an active lifestyle complemented by team events and the Healthy Habits Club.
• Work-Life Fusion: In very dynamic industries such as IT the line between our professional and personal lives can quickly become blurred. Having a one-size-fits-one approach gives us the flexibility to define the work-life dynamic that works for us.
• Emotional Wellbeing: We believe that to maintain our overall health we need to invest in our mental wellbeing just as much as we do in our physical health social connections or in achieving work-life balance.
• Social Wellbeing: As a growing community in a hybrid environment we want to ensure we remain connected not just by the great work we do every day but through our passions and interests.

Remote Work :

Yes

Employment Type :

Full-time