AI Presales Engineer

Responsibilities :

• Identifying business needs and customer pain points that can be addressed through AI

• Designing AI solutions tailored to the customer’s specific requirements, infrastructure, security standards, and budget

• Developing high-level and low-level architecture for AI implementation

• Presenting AI products, technologies, and solutions to customers

• Preparing and demonstrating AI Proofs of Concept (PoC) or prototypes

• Creating technical proposals, solution designs, Bills of Quantities (BoQ), and responses to RFPs/RFIs

• Evaluating technologies, platforms, AI models, and vendors

• Serving as a technical advisor to customers and the sales team

• Calculating ROI, TCO, business benefits, and the feasibility of AI solutions

• Assisting the sales team throughout the tender process, technical negotiations, and deal closure

Technical Qualifications :

AI & Machine Learning Fundamentals

• Understanding of AI, Machine Learning, and Deep Learning concepts.

• Knowledge of model training, inference, fine-tuning, and evaluation.

• Understanding of AI applications for automation, prediction, classification, and recommendation.

Generative AI, Agentic AI & LLM

• Knowledge of LLMs (GPT, Claude, Gemini, Llama, Mistral).

• Understanding of Generative AI, Prompt Engineering, and RAG architecture.

• Knowledge of embeddings, vector databases, and semantic search.

• Understanding of AI Agents, multi-agent systems, LLM orchestration, and API integration.

AI Platform & Infrastructure

• Understanding of GPU servers, computing resources, and AI infrastructure.

• Knowledge of cloud AI platforms and on-premise deployment.

• Familiarity with Docker, Kubernetes, GPU acceleration, MLOps, and LLMOps.

• Understanding of scalability, performance, availability, and capacity planning.

Data Engineering & Database

• Knowledge of SQL, database architecture, data warehouses, and data lakes.

• Understanding of data pipelines and ETL/ELT processes.

• Familiarity with vector databases such as Milvus and Pinecone.

• Understanding of data governance, quality, and security.

AI Security & Governance

• Understanding of AI security, data privacy, and responsible AI.

• Knowledge of AI governance and model risk management.

• Awareness of prompt injection, data leakage, and security threats.

• Understanding of access control, audit trails, and regulatory compliance.

Preferred Qualifications:

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.

• Experience in IT solution architecture, technical consulting, cloud infrastructure, AI/ML, or technical pre-sales.

• Strong analytical, problem-solving, and customer-facing skills.

• Relevant AI, Cloud, or Infrastructure certifications are an advantage

• Strong communication, presentation, customer engagement, technical documentation, and business development suppor