Senior Software Engineer - AI/ML
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Before you apply
LocationLahore, Punjab, Pakistan
Typefull-time
SenioritySenior
Experience4+ yrs
DepartmentCluster Head
Company size501–1,000 people
PostedOct 1, 2026 · 1mo ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field
4+ years of post-graduation professional experience in AI/ML engineering
Demonstrated ownership of production AI systems
Hands-on experience developing LLM or generative AI applications
Strong production-level Python skills
Hands-on experience with PyTorch and/or TensorFlow
Solid knowledge of machine learning, neural networks, NLP, feature engineering, and model optimization
Experience integrating commercial or open-source LLMs, including prompt design, structured outputs, tool calling, context management, and model limitations
Hands-on experience building RAG or semantic-search systems, including embeddings, chunking, retrieval, reranking, and grounding
Experience with vector-search solutions such as pgvector, Pinecone, Weaviate, Qdrant, Milvus, or Elasticsearch
Experience developing and deploying APIs, microservices, or inference services using FastAPI, Flask, Django, or equivalent frameworks
Proficiency in SQL and PostgreSQL or MySQL
Experience deploying AI solutions on AWS, Azure, or Google Cloud
Working knowledge of Git, Docker, automated testing, CI/CD, and MLOps, including experiment tracking, model versioning, and monitoring
Understanding of AI evaluation, regression testing, human review, and security and privacy risks
Ability to own technical decisions, guide engineers, and communicate effectively with clients and cross-functional stakeholders
Experience with AI frameworks such as LangChain, LlamaIndex, or LangGraph
Experience with evaluation tools such as LangSmith, Langfuse, or Ragas
Experience with Hugging Face Transformers, vLLM, Ollama, LoRA/PEFT fine-tuning, or self-hosted models
Familiarity with MLflow, Kubeflow, Kubernetes, Terraform, distributed systems, or GPU acceleration
Experience with data orchestration, asynchronous processing, caching, or messaging, using tools such as Airflow, Redis, Celery, Kafka, or RabbitMQ
Knowledge of advanced retrieval, knowledge graphs, recommendation systems, computer vision, multimodal AI, or A/B testing and product analytics
Skills
Python
PyTorch
TensorFlow
FastAPI
Flask
Django
SQL
PostgreSQL
MySQL
AWS
Azure
Google Cloud
Git
Docker
LangChain
LlamaIndex
LangGraph
LangSmith
Langfuse
Ragas
Hugging Face Transformers
vLLM
Ollama
LoRA
PEFT
MLflow
Kubeflow
Kubernetes
Terraform
Airflow
Redis
Celery
Kafka
RabbitMQ
pgvector
Pinecone
Weaviate
Qdrant
Milvus
Elasticsearch
About the role
Devsinc is hiring a Senior AI/ML Engineer with 4+ years of experience, including development of LLM or generative AI applications. The ideal candidate brings strong machine learning fundamentals and expertise in Python backend services and APIs, RAG, semantic search, AI agents, and scalable ML infrastructure, using commercial and open-source models.
You will own the end-to-end AI lifecycle, from experimentation and evaluation to deployment, optimization, and monitoring, ensuring reliable, scalable, secure, and cost-efficient solutions. The role also involves architectural decisions, mentoring engineers, and collaborating with clients and cross-functional teams to deliver measurable business impact.
Responsibilities
•
Design, develop, and deploy AI/ML and LLM-based applications, including AI agents, tool-using systems, and human-in-the-loop workflows, to solve business problems.
•
Build scalable training, fine-tuning, evaluation, and inference pipelines, with experiment tracking and model versioning.
•
Develop and optimize RAG and semantic-search systems using embeddings, document chunking, vector search, reranking, and grounding.
•
Build backend APIs, microservices, and real-time inference services using Python, FastAPI, Flask, or Django.
•
Improve model quality, latency, throughput, and cost through experimentation, hyperparameter tuning, quantization, batching, and caching.
•
Implement MLOps, automated testing, CI/CD, and production monitoring, supported by evaluation datasets, quality criteria, regression tests, and human review.
•
Guide architectural decisions and cloud deployment, ensuring scalability, reliability, security, and resource efficiency, with safeguards against prompt injection, data leakage, unauthorized access, and unsafe outputs.
•
Evaluate emerging AI technologies and measure feature effectiveness through product analytics or A/B testing.
•
Mentor engineers, collaborate with technical and non-technical stakeholders, and document designs, experiments, and outcomes.
Requirements
•
Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field.
•
4+ years of post-graduation professional experience in AI/ML engineering, with demonstrated ownership of production AI systems and hands-on experience developing LLM or generative AI applications.
•
Strong production-level Python skills, with hands-on experience in PyTorch and/or TensorFlow and solid knowledge of machine learning, neural networks, NLP, feature engineering, and model optimization.
•
Experience integrating commercial or open-source LLMs, including prompt design, structured outputs, tool calling, context management, and model limitations.
•
Hands-on experience building RAG or semantic-search systems, including embeddings, chunking, retrieval, reranking, and grounding, using vector-search solutions such as pgvector, Pinecone, Weaviate, Qdrant, Milvus, or Elasticsearch.
•
Experience developing and deploying APIs, microservices, or inference services using FastAPI, Flask, Django, or equivalent frameworks, with proficiency in SQL and PostgreSQL or MySQL.
•
Experience deploying AI solutions on AWS, Azure, or Google Cloud, with working knowledge of Git, Docker, automated testing, CI/CD, and MLOps, including experiment tracking, model versioning, and monitoring.
•
Understanding of AI evaluation, regression testing, human review, and security and privacy risks.
•
Ability to own technical decisions, guide engineers, and communicate effectively with clients and cross-functional stakeholders.
Preferred Skills & Experience
•
Experience with AI frameworks such as LangChain, LlamaIndex, or LangGraph, and evaluation tools such as LangSmith, Langfuse, or Ragas, is a plus.
•
Experience with Hugging Face Transformers, vLLM, Ollama, LoRA/PEFT fine-tuning, or self-hosted models is preferred.
•
Familiarity with MLflow, Kubeflow, Kubernetes, Terraform, distributed systems, or GPU acceleration is a plus.
•
Experience with data orchestration, asynchronous processing, caching, or messaging, using tools such as Airflow, Redis, Celery, Kafka, or RabbitMQ, is preferred.
•
Knowledge of advanced retrieval, knowledge graphs, recommendation systems, computer vision, multimodal AI, or A/B testing and product analytics is a plus.