Artificial Intelligence Engineer

Role: Artificial Intelligence Engineer

Primary Skill Set

• Generative AI Expertise: Good understanding of modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs and VAEs.

Proven experience in applying these techniques to real-world problems for tasks such as text, code, image, and multimodal generation.

Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like Lang Chain, Lang Graph, Llama Index, and Semantic Kernel.

Hands-on exposure to both API-based (e. g., Claude, GPT, Gemini) and open-source (e. g., Llama, Mistral) LLM-based solution design.

• Agentic AI & Orchestration: Hands-on experience designing autonomous and multi-agent systems that reason, plan, and act using tools.

Familiarity with agentic design patterns (e. g., Re Act, planning, reflection, tool use, human-in-the-loop) and agent frameworks such as Lang Graph, Crew AI, MAF, the Open AI Agents SDK, and Google’s Agent Development Kit (ADK).

Experience building agentic workflows with memory, state management, and reliable multi-step task execution.

• Model Context Protocol (MCP) & Interoperability: Practical understanding of the Model Context Protocol (MCP) for standardized, secure connectivity between LLMs/agents and external tools, data sources, and systems.

Ability to build and consume MCP servers and clients, and to work with MCP primitives such as tools, resources, and prompts.

Awareness of related interoperability standards (e. g., agent-to-agent communication) for composing enterprise-grade agentic systems.

• Agent Skills & Extensibility: Experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources (e. g., SKILL. md-style capability modules) that agents load on demand via progressive disclosure.

Ability to design custom tools, connectors, and skills that let agents perform specialized, domain-specific tasks reliably and safely.

• Retrieval-Augmented Generation (RAG) & Knowledge

Systems: Proven experience designing RAG and knowledge-grounded systems, including chunking strategies, embeddings, vector databases (e. g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and evaluation of retrieval quality.

Familiarity with advanced patterns such as Graph RAG and agentic RAG to reduce hallucination and improve factual grounding.

• Technical Proficiency: An overall understanding of below technologies is required :
• Machine learning algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks
• Data science tools: Num Py, Sci Py, Pandas, Matplotlib, Tensor Flow, Keras
• Natural language processing (NLP): Transformer models, attention mechanisms, word embeddings
• Data ethics: Bias in machine learning, fairness in algorithms
• Foundation models & LLMs: GPT, Claude, Gemini, Llama, Mistral; multimodal and reasoning models; context windows, tokenization, and fine-tuning (Lo RA/PEFT), RLHF/RLAIF concepts
• LLM application & agent frameworks: Lang Chain, Lang Graph, Llama Index, Semantic Kernel, Haystack, Crew AI, Auto Gen
• Interoperability & integration: Model Context Protocol (MCP), function/tool calling, structured outputs, API integration, event-driven and orchestration patterns
• Cloud AI platforms & model hosting: Amazon Bedrock, Azure Open AI / AI Foundry, Google Vertex AI, Hugging Face
• Vector databases & retrieval: Pinecone, Weaviate, Chroma, pgvector, FAISS; embeddings, semantic and hybrid search, reranking
• MLOps / LLMOps & deployment: Docker, Kubernetes, Fast API, CI/CD; observability, tracing, and evaluation tooling (e. g., Lang Smith, Lang Fuse); guardrails and prompt/version management
• Responsible AI & safety: bias and fairness, hallucination mitigation, evaluation, privacy, security, and governance of AI and agentic systems
• Solution

Design: Ability to design end-to-end Generative and Agentic AI solutions, from requirement elicitation and model selection to deployment strategy.

Experience crafting architectures that encompass data preprocessing, RAG pipelines, agent orchestration, MCP-based tool and system integration, model integration, guardrails, and performance, cost, and latency optimization.

• LLMOps, Evaluation & Optimization: Experience operationalizing LLM and agentic applications—building evaluation harnesses and offline/online metrics for quality, groundedness, and safety; implementing observability, tracing, and monitoring; and continuously optimizing accuracy, cost, and latency.

Familiarity with guardrails, red-teaming, and responsible deployment of AI systems in production.

• Communication

Skills: Excellent verbal and written communication skills to engage with clients, articulate technical concepts to non-technical stakeholders, and work collaboratively with cross-functional teams.

Secondary Skill Set

• Domain

Knowledge: Familiarity with the industry domains in which the AI solutions will be applied.

This includes understanding the specific challenges and requirements of different sectors such as healthcare, finance, or manufacturing.

• Project

Management: Basic project management skills to oversee project timelines, milestones, and deliverables.

Experience in coordinating with internal teams and clients to ensure project success.

• Data

Understanding: A foundational grasp of data preprocessing, feature engineering, and data quality assurance processes.

This aids in understanding the data requirements of AI models.

• Responsible AI & Governance: Awareness of AI governance, safety, and compliance considerations—data privacy, security, bias and fairness, transparency, and emerging AI regulations—and how they shape the design and deployment of enterprise Generative and Agentic AI solutions.
• Client

Interaction: Collaborate with client business teams to elicit project requirements and comprehend the desired outcomes.

Translate client needs into technical requirements and AI solution designs.

• Solution

Design: Create comprehensive AI solution designs that address client objectives.

Define the architecture, model selection, and data requirements to ensure successful project execution.

• Agentic
• Solution

Architecture: Architect Generative and Agentic AI solutions—selecting appropriate agent frameworks, RAG strategies, MCP-based integrations, and skills—and define patterns for reliability, safety, human oversight, and scalable production deployment.

• Metrics

Definition: Work closely with clients to define and agree upon measurable metrics that align with business goals.

Ensure that the AI solution's performance is evaluated against these metrics.

• Technical

Implementation: Provide guidance to internal teams on implementing the defined AI solution.

Collaborate with data scientists and engineers to integrate the solution effectively.

• Performance Monitoring: Establish mechanisms to monitor and assess the performance of deployed AI models. Make recommendations for improvements based on observed outcomes.
• Client

Collaboration: Act as a liaison between the client and internal teams, maintaining effective communication throughout the project lifecycle.

Provide regular updates and address any concerns or queries from clients.

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