Experience: 4.00 + years

Salary: Confidential (based on experience)

Expected Notice Period: 7 Days

Shift: (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type: Remote

Placement Type: Full Time Contract for 6 Months(40 hrs a week/160 hrs a month)

(*Note: This is a requirement for one of Uplers client - LL)

What do you need for this opportunity

Must have skills required:

Anthropic, Open AI, token budgeting, AI, LLM, MLOps, Vertex, GCP, Machine Learning, Python

LL is Looking for:

Overview

We are supporting an innovative early-stage SaaS company building a first-of-its-kind AI insights platform for commercial teams. The platform analyses customer conversations alongside sales and marketing data to generate actionable insightsautomating strategy workflows that are traditionally manual. With the MVP built and entering its first customer trials, the team is now looking for an experienced Machine Learning Engineer to optimise and production LLM-driven workflows. This role offers the opportunity to shape how AI operates within the productfrom prompt design and output safety to scalable, reliable inference in real-world environments.

About The Project

This role focuses on delivering practical, high-impact LLM integrations that power the platform's core insight features. Responsibilities include designing robust prompts, deploying inference workflows, and ensuring safe, scalable model behaviour in production.

You will work closely with Product and Backend Engineering to embed AI logic into real user flowsenabling contextual, traceable insights with minimal friction. The current stack includes GCP, Vertex AI, Python-based scripting, and a pragmatic, delivery-focused approach.

This position is ideal for someone who thrives at the intersection of prompt engineering, MLOps, and production deployment, and who is comfortable shaping early-stage infrastructure while maintaining quality, performance, and safety.

Current Phase: Post-MVP Growth & Scaling

With the MVP complete, the focus has shifted from simply getting it to work to:

Improving accuracy
Reducing hallucination risk
Optimising cost and performance

The goal is to evolve early prompt experimentation into a commercially robust, scalable AI engine.

The Role

You will own the AI/ML capability of the platform, ensuring insight generation is:

Clear and consistently structured
Cost-efficient and low-latency
Production-grade and measurable

You will help transition the product from experimental prompts into a reliable, scalable system that supports long-term growth.

Must-Have Experience & Skills

Technical / Product

23+ years of prompt engineering or applied LLM integration experience
Strong understanding of OpenAI, Anthropic, or Vertex AI APIs
Experience deploying LLM inference pipelines in production
Proficiency in Python and cloud-based backend functions
Knowledge of token budgeting, latency constraints, and output control
Experience with prompt testing, risk mitigation, and hallucination reduction
Hands-on exposure to vector databases and RAG architectures (required)
Exposure to embedding-based tagging (bonus)
Understanding of safe, cost-efficient LLM design in early-stage products

Business & Delivery

Ability to translate business language and taxonomies into model prompts
Experience collaborating with Product Managers and domain experts
Strong judgement around end-user expectations, tone, and insight utility
Familiarity with privacy, governance, and enterprise data practices (preferred)

Soft Skills

Strong collaboration skills across Product, Backend, and Leadership teams
High autonomy with a bias for iteration and experimentation
Clear written and verbal communication
Curious, organised, and highly user-focused

Key Responsibilities

Prompt & Model Optimisation: Improve prompt quality, error handling, and structured outputs
Performance Engineering: Optimise token usage, latency, grounding strategies, and hallucination safeguards
Evaluation & Metrics: Define and implement metrics to assess insight quality and reliability
Architecture: Contribute to future architecture decisions for content generation and RAG workflows
Production Lifecycle: Partner with engineering on scalable deployment and model lifecycle management

Success Criteria

LLM outputs are relevant, structured, and aligned with business tone
Inference pipelines are performant, stable, and scalable
Prompts support core insight categories (e.g., sentiment, trends, themes)
Strong collaboration across product and engineering teams
AI infrastructure enables rapid iteration beyond MVP

Additional Information

Equipment: BYOD
Onboarding: Intro sessions with engineering, product, and delivery leads
Eligibility: Candidates must not be based in regions subject to UK financial sanctions

How to apply for this opportunity

Step 1: Click On Apply! And Register or Login on our portal.
Step 2: Complete the Screening Form & Upload updated Resume
Step 3: Increase your chances to get shortlisted & meet the client for the Interview!

About Uplers:

Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.

(Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).

So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!

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