Back to jobs

AI/ML Engineer

Bengaluru, Karnataka, India

Pay
Salary not listed in the saved posting
Work setup
On-site โ€” work setup source
Work setup in the employerโ€™s listing: On-site
From the employerโ€™s posting
Employment
Full-time โ€” employment source
Employment type in the employerโ€™s listing: Full-time
From the employerโ€™s posting
Apply at Weekday AI

What youโ€™ll work on

Full posting
  • Design conversational AI systems with contextual memory and personalized responses

  • Build and optimise RAG pipelines using embeddings, vector databases and retrieval

  • Integrate, deploy and fine-tune open-source LLMs for product and domain needs

  • Build evaluation frameworks for retrieval relevance, memory accuracy and model quality

  • Develop and maintain model-serving APIs, tests, monitoring and deployment workflows

From the employerโ€™s posting
Design and develop conversational AI systems with contextual understanding, long-term memory, and personalised response generation.
Build and optimise RAG pipelines using embeddings, vector databases, structured data, and knowledge retrieval techniques.
Integrate, deploy, and fine-tune open-source LLMs for specific product and domain requirements.
Build evaluation frameworks to measure retrieval relevance, memory accuracy, reasoning quality, response consistency, and overall model performance.
Develop and maintain model-serving APIs, automated tests, monitoring systems, and production deployment workflows.

Tools in this posting

  • Python
  • Neo4j
Source โ€” Tool mentions in context
We are looking for a skilled and innovative AI/ML Engineer to build and scale intelligent AI systems focused on Large Language Models (LLMs), conversational AI, long-term memory, and personalization. The role involves developing advanced retrieval systems, adapting open-source models, and building domain-specific AI capabilities that deliver context-aware, accurate, and personalized user experiences. The ideal candidate will combine strong Python programming skills with hands-on expertise in LLM engineering, Retrieval-Augmented Generation (RAG), vector databases, model fine-tuning, and production deployment. Requirements
- 3+ years of experience in AI/ML engineering, machine learning, NLP, or production AI application development. - Strong proficiency in Python and experience building reliable, production-grade AI systems. - Hands-on experience with LLMs, open-source models, embeddings, vector databases, and RAG architectures.
- Strong analytical and problem-solving skills, with the ability to evaluate AI improvements using measurable outcomes. - Knowledge of LoRA/QLoRA, SLMs, knowledge graphs, Neo4j, or graph-based retrieval is an advantage. - Experience with recommendation systems, ranking models, synthetic data generation, or automated evaluation pipelines is beneficial.

Posting record

Report an error
Status in our records
Live
First seen by us
Oct 15, 2026 ยท 3d ago
Recorded sightings
24
Last seen by us
Oct 17, 2026

These dates show when we found the listing. Check the employerโ€™s website to confirm it is still accepting applications.

Job description

View original posting โ†—

๐—ง๐—ต๐—ถ๐˜€ ๐—ฟ๐—ผ๐—น๐—ฒ ๐—ถ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—ช๐—ฒ๐—ฒ๐—ธ๐—ฑ๐—ฎ๐˜†'๐˜€ ๐—ฐ๐—น๐—ถ๐—ฒ๐—ป๐˜๐˜€

๐—ฆ๐—ฎ๐—น๐—ฎ๐—ฟ๐˜† ๐—ฟ๐—ฎ๐—ป๐—ด๐—ฒ: ๐—ฅ๐˜€ ๐Ÿฏ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ - ๐—ฅ๐˜€ ๐Ÿฐ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ (๐—ถ๐—ฒ ๐—œ๐—ก๐—ฅ ๐Ÿฏ๐Ÿฌ-๐Ÿฐ๐Ÿฌ ๐—Ÿ๐—ฃ๐—”)

Experience: 3+ yrs

Location: Bengaluru

We are looking for a skilled and innovativeย AI/ML Engineerย to build and scale intelligent AI systems focused onย Large Language Models (LLMs), conversational AI, long-term memory, and personalization. The role involves developing advanced retrieval systems, adapting open-source models, and building domain-specific AI capabilities that deliver context-aware, accurate, and personalized user experiences.

The ideal candidate will combine strong Python programming skills with hands-on expertise in LLM engineering, Retrieval-Augmented Generation (RAG), vector databases, model fine-tuning, and production deployment.

Requirements

Key Responsibilities

  • Design and develop conversational AI systems with contextual understanding, long-term memory, and personalised response generation.
  • Build and optimiseย RAG pipelinesย using embeddings, vector databases, structured data, and knowledge retrieval techniques.
  • Develop memory systems for user profiling, information extraction, relevance scoring, retrieval, ranking, and context selection.
  • Integrate, deploy, and fine-tune open-source LLMs for specific product and domain requirements.
  • Develop specialised Small Language Models (SLMs) to support domain-specific reasoning and improve response quality.
  • Create domain-specific datasets and training pipelines for fine-tuning, model adaptation, and continuous learning.
  • Implement LLM orchestration, prompt engineering, classification, ranking, and lightweight machine learning workflows.
  • Build evaluation frameworks to measure retrieval relevance, memory accuracy, reasoning quality, response consistency, and overall model performance.
  • Optimise inference latency, compute utilisation, model reliability, and deployment costs.
  • Develop and maintain model-serving APIs, automated tests, monitoring systems, and production deployment workflows.
  • Investigate AI failures, analyse model outputs, and implement improvements based on measurable performance indicators.
  • Collaborate with Product and Engineering teams to translate user requirements into scalable AI solutions.
  • Evaluate emerging LLM technologies, retrieval architectures, and model optimisation techniques to improve system capabilities.

What Makes You a Great Fit

  • 3+ years of experienceย in AI/ML engineering, machine learning, NLP, or production AI application development.
  • Strong proficiency inย Pythonย and experience building reliable, production-grade AI systems.
  • Hands-on experience with LLMs, open-source models, embeddings, vector databases, and RAG architectures.
  • Understanding of LLM fine-tuning, dataset preparation, model evaluation, and inference optimisation.
  • Experience developing retrieval, conversational memory, context management, or personalisation systems.
  • Familiarity with LLM orchestration frameworks, prompt engineering, and multi-step AI pipelines.
  • Experience deploying models through APIs and implementing testing, monitoring, and production support.
  • Strong analytical and problem-solving skills, with the ability to evaluate AI improvements using measurable outcomes.
  • Knowledge ofย LoRA/QLoRA, SLMs, knowledge graphs, Neo4j, or graph-based retrievalย is an advantage.
  • Experience with recommendation systems, ranking models, synthetic data generation, or automated evaluation pipelines is beneficial.
  • Familiarity with GPU inference optimisation and self-hosted open-source models is desirable.
  • Experience in astrology, symbolic reasoning, or specialised domain-reasoning systems is an added advantage.
  • Strong ownership, communication, and collaboration skills, with the ability to drive AI initiatives from experimentation through production.

Your next step

  • Have your CV and examples of relevant work ready.
  • Check the listed location, eligibility and core experience before starting.
  • Ask the employer about the salary range before committing time to the process.

Complete your application on apply.workable.com. The employerโ€™s form will show what is required.

Already applied? Track this application

Source details

View original posting โ†—

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay

No pay amount identified in the saved description.

Location & working pattern

Bengaluru, Karnataka, India

Working pattern and location restrictions need checking in the full posting.

Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

See how this role fits your experience

Add your resume to compare the roleโ€™s scope, tools and requirements with your experience.

Find answers in the posting

AI
How answers work

AI selects complete passages from this posting. Check them for conditions and exceptions.

Uses this posting and your question. No profile needed.