Job Description

ManagerEXL/M/1435557


ServicesGurgaon
Posted On
28 Jul 2025
End Date
11 Sep 2025
Required Experience
5 - 10 Years



Basic Section
Number Of Positions


1


Band


C1


Band Name


Manager


Cost Code


D013514


Campus/Non Campus


NON CAMPUS


Employment Type


Permanent


Requisition Type


New


Max CTC


1500000.0000 - 2500000.0000


Complexity Level


Not Applicable


Work Type


Hybrid - Working Partly From Home And Partly From Office


Organisational
Group


Analytics


Sub Group


Analytics - UK & Europe


Organization


Services


LOB


Analytics - UK & Europe


SBU


Analytics


Country


India


City


Gurgaon


Center


EXL - Gurgaon Center 38




Skills
Skill


MACHINE LEARNING


Minimum Qualification


B.COM


Certification


No data available

: Senior MLOps Engineer

Position:

Senior MLOps Engineer

Location:

Gurugram

Relevant Experience Required:

6+ years

Employment Type:

Full-time

About the Role

We are seeking a

Senior MLOps Engineer

with deep expertise in

Machine Learning Operations, Data Engineering, and Cloud-Native Deployments

. This role requires building and maintaining

scalable ML pipelines

, ensuring

robust data integration and orchestration

, and enabling

real-time and batch AI systems

in production. The ideal candidate will be skilled in

state-of-the-art MLOps tools

,

data clustering

,

big data frameworks

, and

DevOps best practices

, ensuring high reliability, performance, and security for enterprise AI workloads.

Key Responsibilities

MLOps & Machine Learning Deployment

Design, implement, and maintain

end-to-end ML pipelines

from experimentation to production. Automate

model training, evaluation, versioning, deployment, and monitoring

using MLOps frameworks. Implement

CI/CD pipelines for ML models

(GitHub Actions, GitLab CI, Jenkins, ArgoCD). Monitor ML systems in production for

drift detection, bias, performance degradation, and anomaly detection

. Integrate

feature stores

(Feast, Tecton, Vertex AI Feature Store) for standardized model inputs.

Data Engineering & Integration

Design and implement

data ingestion pipelines

for structured, semi-structured, and unstructured data. Handle

batch and streaming pipelines

with

Apache Kafka, Apache Spark, Apache Flink, Airflow, or Dagster

. Build

ETL/ELT pipelines

for data preprocessing, cleaning, and transformation. Implement

data clustering, partitioning, and sharding strategies

for high availability and scalability. Work with

data warehouses (Snowflake, BigQuery, Redshift)

and

data lakes (Delta Lake, Lakehouse architectures)

. Ensure

data lineage, governance, and compliance

with modern tools (DataHub, Amundsen, Great Expectations).

Cloud & Infrastructure

Deploy ML workloads on

AWS, Azure, or GCP

using

Kubernetes (K8s)

and

serverless computing

(AWS Lambda, GCP Cloud Run). Manage

containerized ML environments

with

Docker, Helm, Kubeflow, MLflow, Metaflow

. Optimize for

cost, latency, and scalability

across distributed environments. Implement

infrastructure as code (IaC)

with Terraform or Pulumi.

Real-Time ML & Advanced Capabilities

Build

real-time inference pipelines

with low latency using gRPC, Triton Inference Server, or Ray Serve. Work on

vector database integrations

(Pinecone, Milvus, Weaviate, Chroma) for AI-powered semantic search. Enable

retrieval-augmented generation (RAG)

pipelines for LLMs. Optimize ML serving with

GPU/TPU acceleration

and

ONNX/TensorRT model optimization

.

Security, Monitoring & Observability

Implement

robust access control, encryption, and compliance

with SOC2/GDPR/ISO27001. Monitor system health with

Prometheus, Grafana, ELK/EFK, and OpenTelemetry

. Ensure

zero-downtime deployments

with blue-green/canary release strategies. Manage

audit trails and explainability

for ML models.

Preferred Skills & Qualifications

Core Technical Skills

Programming:

Python (Pandas, PySpark, FastAPI), SQL, Bash; familiarity with Go or Scala a plus.

MLOps Frameworks:

MLflow, Kubeflow, Metaflow, TFX, BentoML, DVC.

Data Engineering Tools:

Apache Spark, Flink, Kafka, Airflow, Dagster, dbt.

Databases:

PostgreSQL, MySQL, MongoDB, Cassandra, DynamoDB.

Vector Databases:

Pinecone, Weaviate, Milvus, Chroma.

Visualization:

Plotly Dash, Superset, Grafana.

Tech Stack

Orchestration:

Kubernetes, Helm, Argo Workflows, Prefect.

Infrastructure as Code:

Terraform, Pulumi, Ansible.

Cloud Platforms:

AWS (SageMaker, S3, EKS), GCP (Vertex AI, BigQuery, GKE), Azure (ML Studio, AKS).

Model Optimization:

ONNX, TensorRT, Hugging Face Optimum.

Streaming & Real-Time ML:

Kafka, Flink, Ray, Redis Streams.

Monitoring & Logging:

Prometheus, Grafana, ELK, OpenTelemetry.
Workflow
Workflow Type


Back Office