Lad AI Engineer

About Netscribes
Netscribes is a global leader in data, insights, and digital solutions, helping the worlds largest
organizations accelerate growth and innovation. As a growth catalyst, we empower sales,
marketing, product development, and strategy through a unique blend of domain expertise and
technological capabilities. Our end-to-end solutions span data engineering, advanced analytics,
AI, and intelligent automationbuilt to scale and adapt to dynamic business environments. We
partner with clients across the implementation journey, aligning with their ecosystem to
deliver actionable intelligence, operational efficiency, and competitive advantage. For more
information, visit www.netscribes.com.
LoB Overview
Netscribes delivers integrated solutions across content, process, and technology to help global
businesses scale efficiently and stay competitive in fast-moving markets.Our services are
structured across three key areas:
Content Solutions
We specialize in building, managing, and optimizing large-scale content ecosystems for global
brands. Our expertise spans product data enrichment, taxonomy development, catalog and
digital shelf management, content operations, and digital asset optimization. These solutions
help improve product discoverability, streamline content workflows, and enhance customer
experience across digital platforms.
Process Solutions
Our research and insights-driven services support core business processes across marketing,
product, customer experience, and operations. This includes market and competitive research,
campaign analysis, customer intelligence, and content performance tracking. Through
Information Management Services (IMS) and managed service models, we embed scalable,
high-quality support directly into client workflows, ensuring agility, consistency, and speed.
Technology Solutions
Complementing our content and process offerings, we bring advanced technology capabilities
through data engineering, data analytics, and AI. From building unified data platforms and real-
time dashboards to deploying intelligent automation and machine learning models, we help
organizations modernize operations and unlock data-driven decisions at scale.

Profile Description
LOB:
Support | CoE
MANDATORY SKILLS Python Production-grade; clean, modular,
reviewable code; not notebook-first engineering Classical ML, Model
Lifecycle & MLOps End-to-end ownership across the full stack: feature
engineering, model training, evaluation, deployment, versioning,
monitoring, drift detection (data, concept, prediction), retraining triggers,
and CI/CD for ML; MLflow or equivalent; this should be lived delivery
experience must be able to articulate what broke in production and why,
not just what trained well ¢ Agentic AI Engineering Design and build
of multi-agent systems; tool use, agent orchestration, memory
management (short and long-term), state handling; LangGraph, AutoGen,
CrewAI or equivalent; must understand agent failure modes and recovery
patterns, not just happy-path flows ¢ Execution Platforms Hands-on
experience across at least two of: Databricks (Mosaic AI, Agent Bricks,
MLflow), AWS (Bedrock, SageMaker), Azure (AI Foundry, Azure OpenAI,
Azure ML, Azure AI Services); platform-aware and capable of operating
across environments without being locked to one ¢ Foundation Model
Engineering Fine-tuning (full, LoRA, QLoRA), structured prompt
engineering, prompt-to-model drift management, evaluating output quality
across model versions Netscribes Analytics | Data & AI Centre of
Excellence Page 1 JOB DESCRIPTION | Lead Consultant, AI Engineering |
Data & AI CoE ¢ RAG Architecture Strong fundamentals non-negotiable:
document ingestion, chunking strategy, embedding model selection, vector
store integration, re-ranking, retrieval evaluation; Graph RAG and
knowledge graph-based retrieval are expected areas of growth prior
exposure preferred, willingness and ability to lead implementation
required ¢ Vector Databases Hands-on experience with at least one:
Pinecone, Weaviate, Qdrant, or Milvus; must understand trade-offs
between options and be able to make selection decisions based on use case
requirements ¢ LLM Evaluation & Testing Structured evaluation using
frameworks such as RAGAS, TruLens, DeepEval or equivalent; golden set
benchmarking, LLM-as-judge, relevance scoring, hallucination detection;
must be able to define and implement what good looks like quantitatively,
not just qualitatively ¢ AI Governance Baseline At this level of seniority,
expected to speak to model observability, audit trails, responsible AI
considerations, and bias risk in client contexts; formal framework
experience is a bonus, applied judgment is not optional
Role:
Designation:

Employment
Type:
Consultant
Work Mode:
Working Days:
Shift:
Work From Office
5 days (Sat-Sun week off)
Fixed Shift (9 hours)
General Shift
Shift Timings:
Location:
Bengaluru
KEY RESPONSIBILITIES
ML Foundation, Lifecycle & MLOps
¢Own end-to-end ML pipeline design covering data preparation,
feature stores, training, evaluation,
deployment, and monitoring
¢Instrument and manage model drift data drift, concept drift,
prediction drift with automated alerting
and retraining workflows
¢Define and enforce ML engineering standards across the team:
reproducibility, experiment tracking,
Key
code quality
Responsibilities:
Agentic AI Engineering
¢Architect and build multi-agent systems for complex, stateful, multi-step
business workflows
¢Design agent communication patterns, tool registries, memory
management, and failure recovery
¢Move beyond framework defaults understand what the
orchestration layer is doing and when to build
custom
¢Evaluate and select agentic frameworks based on problem
requirements, not familiarity

GenAI & Foundation Model Engineering
¢Select, fine-tune, and serve foundation models appropriate to the
problem; default to fit-for-purpose,
not to the most capable or most expensive option
¢Manage prompt-to-model drift when a model version changes,
understand what breaks and own the
evaluation process
¢Design structured prompt engineering workflows with evaluation
harnesses, not ad hoc iteration
RAG & Knowledge Systems
Netscribes Analytics | Data & AI Centre of Excellence Page 2JOB
DESCRIPTION | Lead Consultant, AI Engineering | Data & AI CoE
¢Design retrieval systems from requirements: choose between naive RAG,
hybrid, Graph RAG, or
knowledge graph-based approaches based on the problem, not the template
¢Select and configure vector databases appropriate to the retrieval use case;
understand latency, scale,
and cost trade-offs
¢Own retrieval quality end-to-end: relevance scoring, hallucination
benchmarking, golden set evaluation
¢Progressively build competency and lead implementation in Graph
RAG and structured knowledge
retrieval
LLM Evaluation & Quality
¢Define and own the evaluation framework for all LLM and agentic AI
solutions from unit evals
through end-to-end system testing
¢Implement LLM Eval tooling (RAGAS, TruLens, DeepEval or
equivalent) as a standard part of the
delivery pipeline, not a post-hoc check

¢Establish golden datasets and ground truth benchmarks per domain;
maintain and evolve them as
models and prompts change
¢Report evaluation outcomes clearly to both technical teams and client
stakeholders
Technical Leadership & Delivery
¢Set the engineering bar for the AI/GenAI team code reviews,
architecture decisions, design
standards
¢Work directly with pre-sales and solution design where technical
depth is required in client
engagements
¢Mentor mid-level engineers; translate ambiguous business
requirements into clear engineering
specifications
¢Represent the team credibly in technical client conversations
across governance, architecture, and
delivery
¢Graduates only (please ignore pursuing/drop out/12th or 10th
pass)
Education: