Roles and Responsibilities 

<ul>
<li>ML Pipeline Architecture &amp; EngineeringScale &amp; Performance: Build and manage complex ML training and inference jobs using Databricks, ensuring high availability, fault tolerance, and data consistency.</li>
</ul>

<ul>
<li>Pipeline Construction: Design and build robust, scalable ML pipelines (ETL, Feature Engineering, Training, Inference) using PySpark, SQL and Databricks</li>
</ul>

<ul>
<li>Cloud-Native API Optimization: Implement model training code optimised for Google Cloud Platform (GCP), leveraging the specific nuances of Google Cloud Storage (GCS) and GKE to maximise throughput, and seamless CI CD. </li>
</ul>

Model Deployment &amp; Lifecycle Management

<ul>
<li>Deployment Strategy: Operationalize ML models by building scalable inference services/APIs on Google Kubernetes Engine (GKE).</li>
</ul>

<ul>
<li>Lifecycle Management: Implement MLOps best practices for model versioning, registry, and monitoring using MLflow (managed within Databricks or GCP). Code review and optimise datascience APIs for best in class practices.</li>
</ul>

<ul>
<li>GenAI Operations: Deploy and manage Small Language Models (LLMs), Vision Language Models (VLMs), and other generative models using tools like Ollama or vLLM/TGI on GPU-accelerated infrastructure.</li>
</ul>

Optimization &amp; Use Case Building

<ul>
<li>Scale Optimization: Optimize PySpark jobs for performance and cost, tuning partitions, memory management, and caching or derived table strategies for massive datasets.</li>
</ul>

<ul>
<li>Use Case Implementation: Partner with Data Scientists to take raw model prototypes and convert them into production-grade systems that solve specific business problems (e.g., Home Page Ranking, Search Ranking, Recommendations, GPU batching).</li>
</ul>

<ul>
<li>Performance Tuning: Monitor and tune model latency and throughput, ensuring our deployments meet strict SLAs.</li>
</ul>

Desired Skills and Experience

<ul>
<li>Experience: 2.5 to 5 years of hands-on experience in Machine Learning Engineering or Data Engineering with an ML focus.</li>
</ul>

Must Have

<ul>
<li>ML Ops: Understanding of Architecture and Design of ML products. Be able to articulate the trade-offs.</li>
</ul>

<ul>
<li>Cloud-Native DevOps: Experience with containerization (Docker) and orchestration (Kubernetes or ECS ) for ML services.</li>
</ul>

<ul>
<li>Data for ML: Breadth of experience working with data blobs, delta lakes, SQL, and storage for ML workflows.</li>
</ul>

<ul>
<li>GCP ML Deployment: Strong hands-on experience deploying models on Google Cloud Platform (Vertex AI or GKE).</li>
</ul>

<ul>
<li>GCP Infrastructure: Familiarity with Kubernetes (GKE) and general GCP infrastructure (IAM, Networking concepts for ML).</li>
</ul>

<ul>
<li>Databricks: Proven experience working with Databricks specifically within the GCP/Azure ecosystem (configuring clusters, Unity Catalog).</li>
</ul>

<ul>
<li>PySpark: Deep understanding of writing, debugging, and optimizing PySpark jobs at scale.</li>
</ul>

<ul>
<li>Core ML Ops: End-to-end experience with Model Training, Model deployment, Lifecycle Management, and Pipeline Orchestration.</li>
</ul>

Highly desired (Tech Stack Expansion)

<ul>
<li>Strong proficiency in SQL for complex data analyses and extraction pipelines/optimisations. </li>
</ul>

<ul>
<li>Experience interacting with noSQL low latency databases such as Aerospike, Redis, MongoDB, ArangoDB.</li>
</ul>

<ul>
<li>Ability to write optimised queries to support and put together A/B analytical dashboards with optimised derived table pipelines and queries</li>
</ul>

Good to Have

<ul>
<li>Azure Context: Familiarity with Azure ML deployment and Databricks on Azure (valuable for understanding our broader ecosystem).</li>
</ul>

<ul>
<li>GenAI Deployment: Hands-on experience deploying LLMs, VLMs, or SLMs on GPUs. Familiarity with serving frameworks like Ollama, TGI, or vLLM.Multi-</li>
</ul>

<ul>
<li>Cloud Experience: Experience working across different cloud providers or transitioning workloads between them.</li>
</ul>

<ul>
<li>Tools &amp; Tech: Familiarity with Kafka, Aerospike, Redis, MongoDB, VectorDBs like Qdrant, Pinecone.</li>
</ul>

Desired Competencies

<ul>
<li>Engineering Rigor: Strong coding standards in Python/Scala with a focus on modularity and testing.</li>
</ul>

<ul>
<li>Collaborative Mindset: Ability to translate "Data Science speak" into "Engineering requirements" and vice versa.</li>
</ul>

<ul>
<li>Problem Solving: A knack for debugging complex distributed systems issues (e.g., Spark OOM errors, K8s pod evictions).</li>
</ul>

<ul>
<li>Business Impact: Understanding of how ML models directly affect business metrics and prioritising engineering tasks accordingly.</li>
</ul>

Machine Learning Engineer

Roles and Responsibilities ML Pipeline Architecture &amp; EngineeringScale &amp; Performance: Build and manage complex ML training and inference jobs using Databricks, ensuring high availability, fault tolerance, and data consistency. Pipeline Construction: Design and build robust, scalable ML pipelines (ETL, Feature Engineering, Training, Inference) using PySpark, SQL and Databricks Cloud-Native API Optimization: Implement model training code optimised for Google Cloud Platform (GCP), leveraging the specific nuances of Google Cloud Storage (GCS) and GKE to maximise throughput, and seamless CI CD. Model Deployment &amp; Lifecycle Management Deployment Strategy: Operationalize ML models by building scalable inference services/APIs on Google Kubernetes Engine (GKE). Lifecycle Management: Implement MLOps best practices for model versioning, registry, and monitoring using MLflow (managed within Databricks or GCP). Code review and optimise datascience APIs for best in class practices. GenAI Operations: Deploy and manage Small Language Models (LLMs), Vision Language Models (VLMs), and other generative models using tools like Ollama or vLLM/TGI on GPU-accelerated infrastructure. Optimization &amp; Use Case Building Scale Optimization: Optimize PySpark jobs for performance and cost, tuning partitions, memory management, and caching or derived table strategies for massive datasets. Use Case Implementation: Partner with Data Scientists to take raw model prototypes and convert them into production-grade systems that solve specific business problems (e.g., Home Page Ranking, Search Ranking, Recommendations, GPU batching). Performance Tuning: Monitor and tune model latency and throughput, ensuring our deployments meet strict SLAs. Desired Skills and Experience Experience: 2.5 to 5 years of hands-on experience in Machine Learning Engineering or Data Engineering with an ML focus. Must Have ML Ops: Understanding of Architecture and Design of ML products. Be able to articulate the trade-offs. Cloud-Native DevOps: Experience with containerization (Docker) and orchestration (Kubernetes or ECS ) for ML services. Data for ML: Breadth of experience working with data blobs, delta lakes, SQL, and storage for ML workflows. GCP ML Deployment: Strong hands-on experience deploying models on Google Cloud Platform (Vertex AI or GKE). GCP Infrastructure: Familiarity with Kubernetes (GKE) and general GCP infrastructure (IAM, Networking concepts for ML). Databricks: Proven experience working with Databricks specifically within the GCP/Azure ecosystem (configuring clusters, Unity Catalog). PySpark: Deep understanding of writing, debugging, and optimizing PySpark jobs at scale. Core ML Ops: End-to-end experience with Model Training, Model deployment, Lifecycle Management, and Pipeline Orchestration. Highly desired (Tech Stack Expansion) Strong proficiency in SQL for complex data analyses and extraction pipelines/optimisations. Experience interacting with noSQL low latency databases such as Aerospike, Redis, MongoDB, ArangoDB. Ability to write optimised queries to support and put together A/B analytical dashboards with optimised derived table pipelines and queries Good to Have Azure Context: Familiarity with Azure ML deployment and Databricks on Azure (valuable for understanding our broader ecosystem). GenAI Deployment: Hands-on experience deploying LLMs, VLMs, or SLMs on GPUs. Familiarity with serving frameworks like Ollama, TGI, or vLLM.Multi- Cloud Experience: Experience working across different cloud providers or transitioning workloads between them. Tools &amp; Tech: Familiarity with Kafka, Aerospike, Redis, MongoDB, VectorDBs like Qdrant, Pinecone. Desired Competencies Engineering Rigor: Strong coding standards in Python/Scala with a focus on modularity and testing. Collaborative Mindset: Ability to translate "Data Science speak" into "Engineering requirements" and vice versa. Problem Solving: A knack for debugging complex distributed systems issues (e.g., Spark OOM errors, K8s pod evictions). Business Impact: Understanding of how ML models directly affect business metrics and prioritising engineering tasks accordingly.

Machine Learning Engineer in Bengaluru (UID-16336)

Job Description

Job Details

Skills Required for Machine Learning Engineer

Job Description for Machine Learning Engineer

Explore More Opportunities