1. Key Responsibilities:

AI Research & Horizon Scanning

- Research emerging AI developments large language models, machine learning, automation, agentic and generative AI and track credible tools, frameworks and vendors.
- Summarize findings into clear, evidence-led briefs that non-technical stakeholders can understand and act on.
- Benchmark AI tools and approaches against real Allied Boston use cases, noting capability, cost, risk and maturity.

Efficiency & Process Enhancement

- Identify where AI can improve efficiency, accuracy and turnaround across GRC delivery and internal workflows.
- Design and run small proof-of-concept experiments to test whether an AI approach delivers measurable benefit before wider adoption.
- Quantify impact time saved, quality improved, effort reduced and recommend whether to scale, refine or stop.

Development Team Enablement

- Design and build AI agents / agentic AI workflows that can reason over enterprise data and take actions, using leading LLM platforms such as OpenAI, Microsoft Copilot and Claude (Anthropic).
- Integrate AI tools with enterprise data connectors SharePoint, OneDrive and similar sources so agents can securely retrieve and act on organizational content.
- Work alongside the development team to help scope, prototype and build new software features and internal tools that embed AI capabilities including RAG, agentic and generative-AI patterns.
- Prepare and structure data, write and refine prompts, and help integrate LLMs, vector search and AI APIs into applications (e.g., FastAPI / Flask backends) under the guidance of senior engineers.
- Build and maintain data-ingestion and workflow-automation pipelines (e.g., n8n or similar) that keep AI tools fed with clean, current data.
- Support testing,



evaluation and validation of AI features checking outputs for accuracy, reliability, bias and edge cases before release.

Responsible & Governed AI

- Apply responsible-AI principles data privacy, fairness, transparency and human oversight — in line with the DPDP Act 2023 and recognized AI governance good practice (e.g., NIST AI Risk Management Framework, ISO/IEC 42001).
- Flag data, security or ethical risks early and help document how AI tools are used, controlled and monitored.

Documentation & Knowledge Sharing

- Maintain clear documentation of experiments, tools, prompts and lessons learned so the wider team can reuse them.
- Share knowledge through short demos, guides and internal sessions that build AI literacy across Allied Boston.

2. Required Skills & Experience:

- 0–2 years of experience, internship or strong academic/project work in AI, machine learning or applied data science.
- Hands-on programming in Python, with familiarity across the AI/ML stack — libraries such as pandas, NumPy and scikit-learn.
- Practical Generative AI / LLM skills — prompt engineering, RAG (retrieval-augmented generation) and semantic search — with hands-on use of leading LLM platforms such as OpenAI, Microsoft Copilot and Claude (Anthropic).




- Ability to create AI agents / agentic AI using frameworks and to connect them to enterprise data sources through connectors like SharePoint and OneDrive.
- Understanding of core ML/DL concepts — regression, decision trees, random forests, transformers — and model evaluation, including explainability techniques such as SHAP.
- Ability to build and expose models through APIs and lightweight backends (e.g., Flask or FastAPI) and to work with data stores and vector databases (e.g., MongoDB, FAISS, Qdrant).
- Solid analytical thinking and the ability to turn research into clear, structured, plain-language summaries.
- A collaborative mindset — comfortable pairing with developers — plus care for accuracy, data privacy and responsible, confidential handling of information.

3. Education & Eligibility:

- Bachelor’s degree in computer science, Data Science, Artificial Intelligence/Machine Learning, Engineering or a related field (or equivalent practical experience).
- Eligibility to work in the applicable jurisdiction; willingness to undergo background verification consistent with local employment law.
- Willingness to work in a hybrid model and travel occasionally to office or client locations as required.

5. Good to Have (Preferred):

- Exposure to cloud AI services — Microsoft Azure / Azure Cognitive (Speech) Services, AWS or GCP.
- Workflow-automation tools such as n8n, and experience building data-ingestion or synchronisation pipelines.
- NLP or speech experience (ASR, diarization, TTS, sentiment analysis) and model-evaluation metrics (WER, CER).
- Front-end skills (React.js, Streamlit, Tailwind CSS) for building simple dashboards and AI-facing interfaces.
- Version control (Git), plus awareness of GRC, cybersecurity, quality or compliance domains — or eagerness to learn them.

📌 AI Analyst (Noida)
🏢 Allied Boston Consultants India
📍 Noida