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Machine Learning - Software Engineer
Machine Learning - Software Engineer

Machine Learning - Software Engineer at Amagi Media Labs · chennai · 5 - 7 years · ₹20L - ₹30L / yr · Profitable · Posted 15 Dec 2021

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Machine Learning - Software Engineer

Rajesh C's profile picture
Posted by Rajesh C
5 - 7 yrs
₹20L - ₹30L / yr
chennai
Skills
skill iconMachine Learning (ML)
skill iconData Science
Natural Language Processing (NLP)
Computer Vision
recommendation algorithm
Job Title: Software Engineer - ML Job Location: Chennai
Job Summary
Condé Nast is seeking an experienced and highly motivated Software engineer-ML who will support
productionizing projects in a databricks environment for the data science team. We expect the person
to be a software/data engineer experienced in building robust ML systems & deploying ML pipelines
in production, Study and transform data science prototypes into an engineering product and is
knowledgeable about machine learning models.
**This role is NOT for building Machine Learning models **
Primary Responsibilities
● Operationalize ML models into production environment(s) by building data
pipelines ● Designing and developing Machine Learning Systems
● Keep abreast of developments in the field
● Come up with engineering ideas to resolve problems faced with respect to ML pipeline ●
Design and code highly scalable, machine learning frameworks processing large volumes of data
● Engineer a near-real-time system that can process massive amounts of data efficiently ●
Collaborate with other Machine Learning Engineers and Data Scientists in architecting &
engineering the solution
● Participate in the entire development lifecycle, from concept to release
● Participate in all phases of quality assurance and defect resolution
Desired Skills & Qualifications
● 5+ years software development experience with highly scalable systems involving
machine learning and big data
● Understanding of data structures, data modeling and software architecture
● Strong software development skills with proficiency in Python/Pyspark
● Experience with Big Data technologies such as Spark, Hadoop
● Familiarity with machine learning frameworks and libraries would be a good-to-have
skill ● Excellent communication skills
● Ability to work in a team
● Outstanding analytical and problem-solving skills
● Applicants should have a Undergraduate/Postgraduate degree in Computer Science or a
related discipline
About Condé Nast
CONDÉ NAST GLOBAL
Condé Nast is a global media house with over a century of distinguished publishing history. With a
portfolio of iconic brands like Vogue, GQ, Vanity Fair, The New Yorker and Bon Appétit, we at Condé Nast
aim to tell powerful, compelling stories of communities, culture and the contemporary world. Our
operations are headquartered in New York and London, with colleagues and collaborators in 32 markets
across the world, including France, Germany, India, China, Japan, Spain, Italy, Russia, Mexico, and Latin
America.
Condé Nast has been raising the industry standards and setting records for excellence in the publishing
space. Today, our brands reach over 1 billion people in print, online, video, and social media.
CONDÉ NAST INDIA (DATA)
Over the years, Condé Nast successfully expanded and diversified into digital, TV, and social platforms -
in other words, a staggering amount of user data. Condé Nast made the right move to invest heavily in
understanding this data and formed a whole new Data team entirely dedicated to data processing,
engineering, analytics, and visualization. This team helps drive engagement, fuel process innovation,
further content enrichment, and increase market revenue. The Data team aimed to create a company
culture where data was the common language and facilitate an environment where insights shared in
real-time could improve performance.
The Global Data team operates out of Los Angeles, New York, Chennai, and London. The team at Condé
Nast Chennai works extensively with data to amplify its brands' digital capabilities and boost online
revenue. We are broadly divided into four groups, Data Intelligence, Data Engineering, Data Science, and
Operations (including Product and Marketing Ops, Client Services) along with Data Strategy and
monetization. The teams-built capabilities and products to create data-driven solutions for better
audience engagement.
What we look forward to:
We want to welcome bright, new minds into our midst and work together to create diverse forms of
self-expression. At Condé Nast, we encourage the imaginative and celebrate the extraordinary. We are a
media company for the future, with a remarkable past. We are Condé Nast, and It Starts Here.
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I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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About Amagi Media Labs

Founded :
2008
Type :
Product
Size :
500-1000
Stage :
Profitable

About

Amagi enables TV networks, OTT platforms, and content owners to transition to cloud technologies for their playout, delivery and monetization needs.
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Job application link : https://grnh.se/z7qx2ehx1us

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Strong Python and the modern ML stack — PyTorch or TensorFlow, scikit-learn, NumPy/Pandas.

Solid grounding in at least one of: computer vision (CNNs, object detection/segmentation, image preprocessing) or time-series /

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Practical MLOps experience: containerization (Docker), model serving, experiment tracking, and deploying on a cloud platform — Azure /

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Comfort working with imperfect, real-world data — labeling strategy, class imbalance, data drift, and validation that reflects production

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Good engineering hygiene (Git, testing, code review) and the ability to write code others can build on.

Nice to Have

Experience with industrial / manufacturing data or regulated environments (pharma, 21 CFR Part 11 awareness).

Hands-on LLM integration experience — RAG, prompt engineering, working with APIs or self-hosted models (vLLM, Qwen, etc.).

Edge deployment experience (running CV models on-device / near the line).

Exposure to data pipeline tooling and orchestration.

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Key Responsibilities


·       Model Architecture & Deployment: Design, train, and deploy production-scale ML/Deep Learning and GenAI systems (computer vision, document intelligence, OCR, NLP, fraud risk classification, and LLM applications).

·       GenAI & LLM Solutions: Develop robust LLM workflows including prompt engineering, fine-tuning, RAG pipelines, semantic search, vector indexing (Pinecone/Milvus/Chroma), and safety guardrails.

·       Pipelines & Engineering: Build performant feature extraction and data pipelines; write modular, vectorized, production-grade Python (NumPy, Pandas) and advanced SQL.

·       MLOps & Monitoring: Establish end-to-end MLOps/LLMOps standards—model registries, CI/CD, experiment tracking, drift detection, A/B testing, latency optimization, and cost governance.

·       Responsible AI & Security: Ensure model decisions comply with enterprise data security, privacy standards, and auditability required by the BFSI sector.

·       Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.


Required Qualifications & Skills


·       Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.

·       Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.

·       GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.

·       Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.

·       Software & Data Engineering: Expert-level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.

·       Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).


Preferred Qualifications

·       Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.

·       Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).

·       Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.

Read more
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Shubham Vishwakarma

Full Stack Developer - Averlon
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